Ai Paper List Grok Rules

	Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. 1-18
		Ahmed Abbas, Paul Swoboda:
ClusterFuG: Clustering Fully connected Graphs by Multicut. 19-30
		Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk:
Generalization on the Unseen, Logic Reasoning and Degree Curriculum. 31-60
		Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. 61-78
		Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. 79-90
		Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier:
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects. 91-132
		Steven Adams, Andrea Patane, Morteza Lahijanian, Luca Laurenti:
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming. 133-151
		Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. 152-168
		Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. 169-195
		Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee:
Global optimality of Elman-type RNNs in the mean-field regime. 196-227
		Pranjal Aggarwal, Ameet Deshpande, Karthik R. Narasimhan:
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification. 228-247
		Mehran Aghabozorgi, Shichong Peng, Ke Li:
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling. 248-264
		Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer:
Scaling Laws for Generative Mixed-Modal Language Models. 265-279
		Anass Aghbalou, Guillaume Staerman:
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. 280-303
		Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa:
Constrained Causal Bayesian Optimization. 304-321
		Elisabeth Agoritsas, Giovanni Catania, Aurélien Decelle, Beatriz Seoane:
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models. 322-336
		Gati V. Aher, Rosa I. Arriaga, Adam Tauman Kalai:
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. 337-371
		Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio:
Interventional Causal Representation Learning. 372-407
		Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus:
Sequential Underspecified Instrument Selection for Cause-Effect Estimation. 408-420
		Matthew Aitchison, Penny Sweetser, Marcus Hutter:
Atari-5: Distilling the Arcade Learning Environment down to Five Games. 421-438
		Naveed Akhtar, Mohammad A. A. K. Jalwana:
Towards credible visual model interpretation with path attribution. 439-457
		Ahmet Alacaoglu, Hanbaek Lyu:
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data. 458-489
		Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Recasting Self-Attention with Holographic Reduced Representations. 490-507
		Wael Alghamdi, Juan Felipe Gómez, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar:
The Saddle-Point Method in Differential Privacy. 508-528
		Christian H. X. Ali Mehmeti-Göpel, Jan Disselhoff:
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think. 529-546
		James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. 547-568
		Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. 569-626
		Baris Alparslan, Sinan Yildirim, S. Ilker Birbil:
Differentially Private Distributed Bayesian Linear Regression with MCMC. 627-641
		Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Scalable Bayesian Online Changepoint Detection. 642-663
		Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl:
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. 664-690
		Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. 691-717
		Alan Nawzad Amin, Eli N. Weinstein, Debora Susan Marks:
A Kernelized Stein Discrepancy for Biological Sequences. 718-767
		Philip Amortila, Nan Jiang, Csaba Szepesvári:
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. 768-790
		Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko:
Meta Optimal Transport. 791-813
		Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm:
Near-Optimal Φ-Regret Learning in Extensive-Form Games. 814-839
		Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A Modern Look at the Relationship between Sharpness and Generalization. 840-902
		Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with Large Step Sizes Learns Sparse Features. 903-925
		Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh:
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series. 926-951
		Antonios Antoniadis, Joan Boyar, Marek Eliás, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon:
Paging with Succinct Predictions. 952-968
		Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon:
Mixing Predictions for Online Metric Algorithms. 969-983
		Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Exponential Smoothing for Off-Policy Learning. 984-1017
		Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. 1018-1040
		Sohei Arisaka, Qianxiao Li:
Principled Acceleration of Iterative Numerical Methods Using Machine Learning. 1041-1059
		Raman Arora, Raef Bassily, Tomás González, Cristóbal Guzmán, Michael Menart, Enayat Ullah:
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization. 1060-1092
		Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. 1093-1106
		Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. 1107-1120
		Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. 1121-1146
		Amit Attia, Tomer Koren:
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. 1147-1171
		Idan Attias, Steve Hanneke:
Adversarially Robust PAC Learnability of Real-Valued Functions. 1172-1199
		Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas:
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. 1200-1217
		Yuval Atzmon, Eli A. Meirom, Shie Mannor, Gal Chechik:
Learning to Initiate and Reason in Event-Driven Cascading Processes. 1218-1243
		Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret:
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm. 1244-1275
		Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou:
Dirichlet Diffusion Score Model for Biological Sequence Generation. 1276-1301
		Kyriakos Axiotis, Maxim Sviridenko:
Gradient Descent Converges Linearly for Logistic Regression on Separable Data. 1302-1319
		Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Naive imputation implicitly regularizes high-dimensional linear models. 1320-1340
		Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. 1341-1360
		Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica:
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. 1361-1395
		Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang:
Personalized Subgraph Federated Learning. 1396-1415
		Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli:
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language. 1416-1429
		Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler:
Efficient preconditioned stochastic gradient descent for estimation in latent variable models. 1430-1453
		Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. 1454-1471
		Yushi Bai, Xin Lv, Juanzi Li, Lei Hou:
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. 1472-1491
		Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Muhammad Shahroz Khan, Soheil Kolouri:
Linear optimal partial transport embedding. 1492-1520
		Justin Baker, Qingsong Wang, Cory D. Hauck, Bao Wang:
Implicit Graph Neural Networks: A Monotone Operator Viewpoint. 1521-1548
		Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. 1549-1563
		Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer:
Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures. 1564-1576
		Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine:
Efficient Online Reinforcement Learning with Offline Data. 1577-1594
		Marin Ballu, Quentin Berthet:
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes. 1595-1613
		András Balogh, Márk Jelasity:
On the Functional Similarity of Robust and Non-Robust Neural Representations. 1614-1635
		Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. 1636-1659
		Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh:
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time. 1660-1691
		Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. 1692-1717
		Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He:
Optimizing the Collaboration Structure in Cross-Silo Federated Learning. 1718-1736
		Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel:
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation. 1737-1752
		Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. 1753-1800
		Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra:
Interpretable Neural-Symbolic Concept Reasoning. 1801-1825
		Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina:
Moccasin: Efficient Tensor Rematerialization for Neural Networks. 1826-1837
		Raef Bassily, Ziteng Sun:
User-level Private Stochastic Convex Optimization with Optimal Rates. 1838-1851
		Soumya Basu, Ankit Singh Rawat, Manzil Zaheer:
A Statistical Perspective on Retrieval-Based Models. 1852-1886
		Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang:
Human-Timescale Adaptation in an Open-Ended Task Space. 1887-1935
		Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A Kernel Stein Test of Goodness of Fit for Sequential Models. 1936-1953
		Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. 1954-1977
		Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. 1978-2002
		Daniel Beechey, Thomas M. S. Smith, Özgür Simsek:
Explaining Reinforcement Learning with Shapley Values. 2003-2014
		Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov:
TIDE: Time Derivative Diffusion for Deep Learning on Graphs. 2015-2030
		Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. 2031-2049
		Christopher M. Bender, Yifeng Shi, Marc Niethammer, Junier Oliva:
Continuously Parameterized Mixture Models. 2050-2062
		Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny:
Controllable Neural Symbolic Regression. 2063-2077
		Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. 2078-2091
		M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys:
Certified Robust Neural Networks: Generalization and Corruption Resistance. 2092-2112
		Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. 2113-2163
		Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion. 2164-2183
		Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Constrained Phi-Equilibria. 2184-2205
		Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. 2206-2233
		Arturs Berzins:
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision. 2234-2244
		Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis:
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. 2245-2271
		Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. 2272-2288
		Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol:
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. 2289-2312
		Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Bandit Online Linear Optimization with Hints and Queries. 2313-2336
		Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai:
Improved Online Conformal Prediction via Strongly Adaptive Online Learning. 2337-2363
		Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri:
Data-Copying in Generative Models: A Formal Framework. 2364-2396
		Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, Oskar van der Wal:
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. 2397-2430
		Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan:
StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes. 2431-2451
		Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. 2452-2470
		Julian Bitterwolf, Maximilian Müller, Matthias Hein:
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. 2471-2506
		Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf:
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. 2507-2527
		Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang:
Understanding Oversquashing in GNNs through the Lens of Effective Resistance. 2528-2547
		Charlie Blake, Douglas Orr, Carlo Luschi:
Unit Scaling: Out-of-the-Box Low-Precision Training. 2548-2576
		Matthieu Blanke, Marc Lelarge:
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems. 2577-2591
		Markus Bläser:
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits. 2592-2602
		Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux:
Learning the Dynamics of Sparsely Observed Interacting Systems. 2603-2640
		Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi:
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions. 2641-2688
		Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy:
Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist. 2689-2711
		David Boetius, Stefan Leue, Tobias Sutter:
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. 2712-2737
		Simone Bombari, Shayan Kiyani, Marco Mondelli:
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. 2738-2776
		Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. 2777-2805
		Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. 2806-2823
		Victor Boone, Bruno Gaujal:
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning. 2824-2856
		Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete:
Model-agnostic Measure of Generalization Difficulty. 2857-2884
		Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. 2885-2913
		Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou:
In Search for a Generalizable Method for Source Free Domain Adaptation. 2914-2931
		Adam Bouland, Yosheb M. Getachew, Yujia Jin, Aaron Sidford, Kevin Tian:
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. 2932-2952
		Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, Thomas Serre:
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? 2953-3002
		Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. 3003-3020
		Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting:
ILLUME: Rationalizing Vision-Language Models through Human Interactions. 3021-3037
		Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. 3038-3062
		Gecia Bravo Hermsdorff:
Quantifying Human Priors over Social and Navigation Networks. 3063-3105
		Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar:
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. 3106-3147
		Trenton Bricken, Rylan Schaeffer, Bruno A. Olshausen, Gabriel Kreiman:
Emergence of Sparse Representations from Noise. 3148-3191
		Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. 3192-3218
		Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao:
Machine Learning Force Fields with Data Cost Aware Training. 3219-3232
		Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii:
Label differential privacy and private training data release. 3233-3251
		Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. 3252-3298
		Federico Cacciamani, Matteo Castiglioni, Nicola Gatti:
Online Mechanism Design for Information Acquisition. 3299-3326
		Vittorio Caggiano, Sudeep Dasari, Vikash Kumar:
MyoDex: A Generalizable Prior for Dexterous Manipulation. 3327-3346
		Francesco Cagnetta, Alessandro Favero, Matthieu Wyart:
What Can Be Learnt With Wide Convolutional Neural Networks? 3347-3379
		Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang:
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. 3380-3407
		Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang:
On the Connection Between MPNN and Graph Transformer. 3408-3430
		Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen:
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition. 3431-3441
		Yuchao Cai, Yuheng Ma, Yiwei Dong, Hanfang Yang:
Extrapolated Random Tree for Regression. 3442-3468
		Xufeng Cai, Chaobing Song, Stephen J. Wright, Jelena Diakonikolas:
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization. 3469-3494
		Ruisi Cai, Zhenyu Zhang, Zhangyang Wang:
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? 3495-3506
		Yang Cai, Weiqiang Zheng:
Doubly Optimal No-Regret Learning in Monotone Games. 3507-3524
		Mine Melodi Caliskan, Francesco Chini, Setareh Maghsudi:
Multi-Agent Learning from Learners. 3525-3540
		Yadi Cao, Menglei Chai, Minchen Li, Chenfanfu Jiang:
Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network. 3541-3558
		Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Tobias Schaefer, Matthias Katzfuss:
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. 3559-3576
		Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui:
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation. 3577-3598
		Steven Cao, Percy Liang, Gregory Valiant:
One-sided Matrix Completion from Two Observations Per Row. 3599-3624
		Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson:
State and parameter learning with PARIS particle Gibbs. 3625-3675
		Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer:
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning. 3676-3713
		Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier:
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning. 3714-3731
		Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T. J. Spaan:
Scalable Safe Policy Improvement via Monte Carlo Tree Search. 3732-3756
		Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson:
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. 3757-3781
		Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. 3782-3814
		Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro:
Learning Globally Smooth Functions on Manifolds. 3815-3854
		Jaeyoung Cha, Jaewook Lee, Chulhee Yun:
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. 3855-3912
		Jaehoon Cha, Jeyan Thiyagalingam:
Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations. 3913-3948
		Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. 3949-3978
		Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari:
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. 3979-4008
		Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa:
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. 4009-4034
		Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. 4035-4054
		Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan:
Muse: Text-To-Image Generation via Masked Generative Transformers. 4055-4075
		Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee:
On Investigating the Conservative Property of Score-Based Generative Models. 4076-4095
		Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and private stochastic linear bandits. 4096-4115
		Anamay Chaturvedi, Huy L. Nguyen, Thy Dinh Nguyen:
Streaming Submodular Maximization with Differential Privacy. 4116-4143
		Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz:
Why does Throwing Away Data Improve Worst-Group Error? 4144-4188
		Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant:
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits. 4189-4217
		Fengdi Che, Gautham Vasan, A. Rupam Mahmood:
Correcting discount-factor mismatch in on-policy policy gradient methods. 4218-4240
		Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, Jun Huan:
Fast Federated Machine Unlearning with Nonlinear Functional Theory. 4241-4268
		David Cheikhi, Daniel Russo:
On the Statistical Benefits of Temporal Difference Learning. 4269-4293
		Zhengdao Chen:
Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces. 4294-4329
		Lei Chen, Joan Bruna:
Beyond the Edge of Stability via Two-step Gradient Updates. 4330-4391
		Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, Tien-Hao Chang:
Trompt: Towards a Better Deep Neural Network for Tabular Data. 4392-4434
		Du Chen, Geoffrey A. Chua:
Differentially Private Stochastic Convex Optimization under a Quantile Loss Function. 4435-4461
		Sitan Chen, Giannis Daras, Alex Dimakis:
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. 4462-4484
		Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. 4485-4513
		Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines. 4514-4528
		Yanzhi Chen, Michael U. Gutmann, Adrian Weller:
Is Learning Summary Statistics Necessary for Likelihood-free Inference? 4529-4544
		Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang:
Subequivariant Graph Reinforcement Learning in 3D Environments. 4545-4565
		Hanxiao Chen, Meng Hao, Hongwei Li, Kangjie Chen, Guowen Xu, Tianwei Zhang, Xilin Zhang:
GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. 4566-4584
		Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu:
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. 4585-4610
		Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. 4611-4622
		Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, Zhi-Ming Ma:
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization. 4623-4640
		Xuxing Chen, Minhui Huang, Shiqian Ma, Krishna Balasubramanian:
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity. 4641-4671
		Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. 4672-4712
		Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Sample Complexity of Probability Divergences under Group Symmetry. 4713-4734
		Hongrui Chen, Holden Lee, Jianfeng Lu:
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions. 4735-4763
		Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao:
Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers. 4764-4803
		Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang:
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. 4804-4825
		Keyi Chen, Francesco Orabona:
Generalized Implicit Follow-The-Regularized-Leader. 4826-4838
		Dexiong Chen, Paolo Pellizzoni, Karsten M. Borgwardt:
Fisher Information Embedding for Node and Graph Learning. 4839-4855
		Jiaxuan Chen, Yu Qi, Gang Pan:
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues. 4856-4866
		Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha:
Stratified Adversarial Robustness with Rejection. 4867-4894
		Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal:
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. 4895-4920
		Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu:
Model Transferability with Responsive Decision Subjects. 4921-4952
		Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta:
Layered State Discovery for Incremental Autonomous Exploration. 4953-5001
		Sijia Chen, Wei-Wei Tu, Peng Zhao, Lijun Zhang:
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. 5002-5035
		Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang Wang:
Learning to Optimize Differentiable Games. 5036-5051
		Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng:
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets. 5052-5086
		Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan:
Semi-Offline Reinforcement Learning for Optimized Text Generation. 5087-5103
		Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. 5104-5161
		Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen:
Implicit Neural Spatial Representations for Time-dependent PDEs. 5162-5177
		Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, Furu Wei:
BEATs: Audio Pre-Training with Acoustic Tokenizers. 5178-5193
		Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang:
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model. 5194-5218
		Lesi Chen, Jing Xu, Luo Luo:
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. 5219-5233
		Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li:
Efficient Personalized Federated Learning via Sparse Model-Adaptation. 5234-5256
		Yifan Chen, Rentian Yao, Yun Yang, Jie Chen:
A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening. 5257-5281
		Dangxing Chen, Weicheng Ye:
How to address monotonicity for model risk management? 5282-5295
		Xin Chen, Yicheng Zeng, Siyue Yang, Qiang Sun:
Sketched Ridgeless Linear Regression: The Role of Downsampling. 5296-5326
		Dingyang Chen, Qi Zhang:
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. 5327-5350
		Can Chen, Yingxue Zhang, Xue Liu, Mark Coates:
Bidirectional Learning for Offline Model-based Biological Sequence Design. 5351-5366
		Tianqi Chen, Mingyuan Zhou:
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling. 5367-5382
		Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, Claire Cui:
Lifelong Language Pretraining with Distribution-Specialized Experts. 5383-5395
		Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu:
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. 5396-5427
		Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng:
Weakly Supervised Regression with Interval Targets. 5428-5448
		Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li:
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion. 5449-5471
		Minhao Cheng, Rui Min, Haochen Sun, Pin-Yu Chen:
Identification of the Adversary from a Single Adversarial Example. 5472-5484
		Xiaotong Cheng, Cheng Pan, Setareh Maghsudi:
Parallel Online Clustering of Bandits via Hedonic Game. 5485-5503
		Yong Cheng, Yu Zhang, Melvin Johnson, Wolfgang Macherey, Ankur Bapna:
Mu2SLAM: Multitask, Multilingual Speech and Language Models. 5504-5520
		Duo Cheng, Xingyu Zhou, Bo Ji:
Understanding the Role of Feedback in Online Learning with Switching Costs. 5521-5543
		David Chiang, Peter Cholak, Anand Pillay:
Tighter Bounds on the Expressivity of Transformer Encoders. 5544-5562
		Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. 5563-5599
		Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. 5600-5615
		Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. 5616-5630
		Hong-Ming Chiu, Richard Y. Zhang:
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations. 5631-5660
		Cheol Jun Cho, Edward F. Chang, Gopala Krishna Anumanchipalli:
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data. 5661-5676
		Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. 5677-5721
		Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho:
GREAD: Graph Neural Reaction-Diffusion Networks. 5722-5747
		Hee Min Choi, Hyoa Kang, Dokwan Oh:
Is Overfitting Necessary for Implicit Video Representation? 5748-5770
		Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik, Min-hwan Oh:
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model. 5771-5786
		Jaemoo Choi, Yesom Park, Myungjoo Kang:
Restoration based Generative Models. 5787-5816
		Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash:
Concept-based Explanations for Out-of-Distribution Detectors. 5817-5837
		Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya:
Active causal structure learning with advice. 5838-5867
		Davin Choo, Kirankumar Shiragur:
New metrics and search algorithms for weighted causal DAGs. 5868-5903
		Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery:
Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions. 5904-5923
		Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. 5924-5963
		Krzysztof Marcin Choromanski:
Taming graph kernels with random features. 5964-5977
		Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller:
Efficient Graph Field Integrators Meet Point Clouds. 5978-6004
		Era Choshen, Aviv Tamar:
ContraBAR: Contrastive Bayes-Adaptive Deep RL. 6005-6027
		Rishav Chourasia, Neil Shah:
Forget Unlearning: Towards True Data-Deletion in Machine Learning. 6028-6073
		Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen:
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. 6074-6114
		Rhea Chowers, Yair Weiss:
What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective. 6115-6139
		Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica:
Unifying Molecular and Textual Representations via Multi-task Language Modelling. 6140-6157
		Xu Chu, Yujie Jin, Xin Wang, Shanghang Zhang, Yasha Wang, Wenwu Zhu, Hong Mei:
Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks. 6158-6184
		Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, Tyng-Luh Liu:
Shape-Guided Dual-Memory Learning for 3D Anomaly Detection. 6185-6194
		Jianing Chu, Shu Yang, Wenbin Lu:
Multiply Robust Off-policy Evaluation and Learning under Truncation by Death. 6195-6227
		Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. 6228-6242
		Bilal Chughtai, Lawrence Chan, Neel Nanda:
A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations. 6243-6267
		Jase Clarkson:
Distribution Free Prediction Sets for Node Classification. 6268-6278
		Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani:
Sequential Strategic Screening. 6279-6295
		David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon:
Few-Sample Feature Selection via Feature Manifold Learning. 6296-6319
		Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha:
Spatial Implicit Neural Representations for Global-Scale Species Mapping. 6320-6342
		Andrea Coletta, Svitlana Vyetrenko, Tucker Balch:
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs. 6343-6363
		Armand Comas Massague, Yilun Du, Christian Fernandez Lopez, Sandesh Ghimire, Mario Sznaier, Joshua B. Tenenbaum, Octavia I. Camps:
Inferring Relational Potentials in Interacting Systems. 6364-6383
		Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye:
Task-specific experimental design for treatment effect estimation. 6384-6401
		Elisabetta Cornacchia, Elchanan Mossel:
A Mathematical Model for Curriculum Learning for Parities. 6402-6423
		Ian Connick Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee:
Learning to Maximize Mutual Information for Dynamic Feature Selection. 6424-6447
		Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang:
Rethinking Weak Supervision in Helping Contrastive Learning. 6448-6467
		Hugo Cui, Florent Krzakala, Lenka Zdeborová:
Bayes-optimal Learning of Deep Random Networks of Extensive-width. 6468-6521
		Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang:
A General Representation Learning Framework with Generalization Performance Guarantees. 6522-6544
		Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, Alois Knoll:
IRNeXt: Rethinking Convolutional Network Design for Image Restoration. 6545-6564
		Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. 6565-6590
		Yiming Cui, Linjie Yang, Haichao Yu:
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation. 6591-6602
		Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. 6603-6622
		Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. 6623-6642
		Ashok Cutkosky, Harsh Mehta, Francesco Orabona:
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. 6643-6670
		Marco Cuturi, Michal Klein, Pierre Ablin:
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. 6671-6682
		Edwige Cyffers, Aurélien Bellet, Debabrota Basu:
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. 6683-6711
		Yanbo Dai, Songze Li:
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. 6712-6725
		Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. 6726-6759
		Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. 6760-6785
		Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. 6786-6817
		Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. 6818-6835
		Hadi Daneshmand, Jason D. Lee, Chi Jin:
Efficient displacement convex optimization with particle gradient descent. 6836-6854
		Ronghao Dang, Lu Chen, Liuyi Wang, Zongtao He, Chengju Liu, Qijun Chen:
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation. 6855-6872
		Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. 6873-6947
		Christoph Dann, Yishay Mansour, Mehryar Mohri:
Reinforcement Learning Can Be More Efficient with Multiple Rewards. 6948-6967
		Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. 6968-7008
		Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia:
Image generation with shortest path diffusion. 7009-7024
		Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. 7025-7065
		Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. 7066-7101
		Rudrajit Das, Sujay Sanghavi:
Understanding Self-Distillation in the Presence of Label Noise. 7102-7140
		Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das:
Interval Bound Interpolation for Few-shot Learning with Few Tasks. 7141-7166
		Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. 7167-7204
		Sami Davies, Benjamin Moseley, Heather Newman:
Fast Combinatorial Algorithms for Min Max Correlation Clustering. 7205-7230
		Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Predictive Flows for Faster Ford-Fulkerson. 7231-7248
		Thomas Davies, Zhengchao Wan, Rubén J. Sánchez-García:
The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data. 7249-7263
		Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. 7264-7302
		Hassan Dbouk, Naresh R. Shanbhag:
On the Robustness of Randomized Ensembles to Adversarial Perturbations. 7303-7328
		Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen:
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. 7329-7342
		Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk:
Continuous Spatiotemporal Transformer. 7343-7365
		Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein:
The Value of Out-of-Distribution Data. 7366-7389
		Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker:
High Fidelity Image Counterfactuals with Probabilistic Causal Models. 7390-7425
		Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla:
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. 7426-7448
		Aaron Defazio, Konstantin Mishchenko:
Learning-Rate-Free Learning by D-Adaptation. 7449-7479
		Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby:
Scaling Vision Transformers to 22 Billion Parameters. 7480-7512
		Blaise Delattre, Quentin Barthélemy, Alexandre Araujo, Alexandre Allauzen:
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration. 7513-7532
		Emir Demirovic, Emmanuel Hebrard, Louis Jean:
Blossom: an Anytime Algorithm for Computing Optimal Decision Trees. 7533-7562
		Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. 7563-7595
		Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng:
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning. 7596-7616
		Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. 7617-7644
		Shikuang Deng, Hao Lin, Yuhang Li, Shi Gu:
Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks. 7645-7657
		Weijian Deng, Yumin Suh, Stephen Gould, Liang Zheng:
Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation. 7658-7674
		Ailin Deng, Miao Xiong, Bryan Hooi:
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement. 7675-7693
		Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam:
Hyperbolic Image-text Representations. 7694-7731
		Aditya Desai, Keren Zhou, Anshumali Shrivastava:
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. 7732-7749
		Tim Dettmers, Luke Zettlemoyer:
The case for 4-bit precision: k-bit Inference Scaling Laws. 7750-7774
		Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova:
Fairness in Matching under Uncertainty. 7775-7794
		Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse:
Efficient Parametric Approximations of Neural Network Function Space Distance. 7795-7812
		Victor Dheur, Souhaib Ben Taieb:
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. 7813-7836
		Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. 7837-7864
		Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. 7865-7885
		Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. 7886-7921
		Ilias Diakonikolas, Daniel Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. 7922-7938
		Nathaniel Lee Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. 7939-7959
		Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim:
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. 7960-7991
		Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. 7992-8014
		Nikolaos Dimitriadis, Pascal Frossard, François Fleuret:
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models. 8015-8052
		Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone:
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models. 8053-8066
		Lisang Ding, Kexin Jin, Bicheng Ying, Kun Yuan, Wotao Yin:
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm. 8067-8089
		Zheng Ding, Jieke Wang, Zhuowen Tu:
Open-Vocabulary Universal Image Segmentation with MaskCLIP. 8090-8102
		Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu:
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning. 8103-8119
		AnhDung Dinh, Daochang Liu, Chang Xu:
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance. 8120-8137
		Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-Order Optimization with Lazy Hessians. 8138-8161
		Nikita Doikov, Anton Rodomanov:
Polynomial Preconditioning for Gradient Methods. 8162-8187
		Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf:
On Data Manifolds Entailed by Structural Causal Models. 8188-8201
		Mingze Dong, Yuval Kluger:
Towards Understanding and Reducing Graph Structural Noise for GNNs. 8202-8226
		Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang, Yanzhi Wang, Chih-Hsien Chou:
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection. 8227-8243
		Chengyu Dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao, Xiaodong Liu:
Understand and Modularize Generator Optimization in ELECTRA-style Pretraining. 8244-8259
		Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. 8260-8275
		Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh:
PASTA: Pessimistic Assortment Optimization. 8276-8295
		Yijun Dong, Yuege Xie, Rachel A. Ward:
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift. 8296-8316
		Jialin Dong, Lin Yang:
Does Sparsity Help in Learning Misspecified Linear Bandits? 8317-8333
		Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang:
Symmetry-Aware Robot Design with Structured Subgroups. 8334-8355
		Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy:
DoCoFL: Downlink Compression for Cross-Device Federated Learning. 8356-8388
		Will Dorrell, Maria Yuffa, Peter E. Latham:
Meta-Learning the Inductive Bias of Simple Neural Circuits. 8389-8402
		Vishwaraj Doshi, Jie Hu, Do Young Eun:
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains. 8403-8423
		Matthew Dowling, Yuan Zhao, Il Memming Park:
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains. 8424-8448
		Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe:
On the Convergence Rate of Gaussianization with Random Rotations. 8449-8468
		Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. 8469-8488
		Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. 8489-8510
		Yihan Du, Longbo Huang, Wen Sun:
Multi-task Representation Learning for Pure Exploration in Linear Bandits. 8511-8564
		Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin:
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows. 8565-8584
		Jin-Hong Du, Pratik Patil, Arun K. Kuchibhotla:
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation. 8585-8631
		Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan, Yue Wang, Yang Yuan, Hang Zhao:
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning. 8632-8656
		Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas:
Guiding Pretraining in Reinforcement Learning with Large Language Models. 8657-8677
		Weitao Du, He Zhang, Tao Yang, Yuanqi Du:
A Flexible Diffusion Model. 8678-8696
		Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang:
Fast Excess Risk Rates via Offset Rademacher Complexity. 8697-8716
		Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu:
Are Diffusion Models Vulnerable to Membership Inference Attacks? 8717-8730
		Zhibin Duan, Xinyang Liu, Yudi Su, Yishi Xu, Bo Chen, Mingyuan Zhou:
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process. 8731-8746
		Zhijian Duan, Yunxuan Ma, Xiaotie Deng:
Are Equivariant Equilibrium Approximators Beneficial? 8747-8778
		Yann Dubois, Tatsunori Hashimoto, Percy Liang:
Evaluating Self-Supervised Learning via Risk Decomposition. 8779-8820
		Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam:
Fully Dynamic Submodular Maximization over Matroids. 8821-8835
		Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang:
Optimal No-Regret Learning for One-Sided Lipschitz Functions. 8836-8850
		Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori:
Integrating Prior Knowledge in Contrastive Learning with Kernel. 8851-8878
		Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic:
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. 8879-8901
		Lyndon R. Duong, David Lipshutz, David J. Heeger, Dmitri B. Chklovskii, Eero P. Simoncelli:
Adaptive Whitening in Neural Populations with Gain-modulating Interneurons. 8902-8921
		Benjamin Dupuis, George Deligiannidis, Umut Simsekli:
Generalization Bounds using Data-Dependent Fractal Dimensions. 8922-8968
		Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Population Based Training. 8969-8989
		Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. 8990-9012
		Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick:
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. 9013-9033
		Javier E. Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin:
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces. 9034-9059
		Eduard Eiben, Robert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider:
The Computational Complexity of Concise Hypersphere Classification. 9060-9070
		Floor Eijkelboom, Rob Hesselink, Erik J. Bekkers:
E(n) Equivariant Message Passing Simplicial Networks. 9071-9081
		Itay Eilat, Nir Rosenfeld:
Performative Recommendation: Diversifying Content via Strategic Incentives. 9082-9103
		Theresa Eimer, Marius Lindauer, Roberta Raileanu:
Hyperparameters in Reinforcement Learning and How To Tune Them. 9104-9149
		Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization over a Matroid Constraint. 9150-9171
		Marwa El Halabi, George Orfanides, Tim Hoheisel:
Difference of submodular minimization via DC programming. 9172-9201
		Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. 9202-9223
		Moshe Eliasof, Lars Ruthotto, Eran Treister:
Improving Graph Neural Networks with Learnable Propagation Operators. 9224-9245
		Dor Elimelech, Wasim Huleihel:
Phase Transitions in the Detection of Correlated Databases. 9246-9266
		Yury Elkin, Vitaliy Kurlin:
A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree. 9267-9311
		Mark Endo, Joy Hsu, Jiaman Li, Jiajun Wu:
Motion Question Answering via Modular Motion Programs. 9312-9328
		Joseph Enguehard:
Learning Perturbations to Explain Time Series Predictions. 9329-9342
		Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour:
Regret Minimization and Convergence to Equilibria in General-sum Markov Games. 9343-9373
		Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin:
Delayed Bandits: When Do Intermediate Observations Help? 9374-9395
		Carlos Esteves, Jean-Jacques E. Slotine, Ameesh Makadia:
Scaling Spherical CNNs. 9396-9411
		Mathieu Even:
Stochastic Gradient Descent under Markovian Sampling Schemes. 9412-9439
		Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry:
Continual Learning in Linear Classification on Separable Data. 9440-9484
		Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. 9485-9507
		Lukas Faber, Roger Wattenhofer:
Neural Status Registers. 9508-9522
		Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. 9523-9546
		Gregory Faletto, Jacob Bien:
Predicting Rare Events by Shrinking Towards Proportional Odds. 9547-9602
		Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson:
Free-Form Variational Inference for Gaussian Process State-Space Models. 9603-9622
		Ying Fan, Kangwook Lee:
Optimizing DDPM Sampling with Shortcut Fine-Tuning. 9623-9639
		Chenglin Fan, Ping Li, Xiaoyun Li:
LSDS++ : Dual Sampling for Accelerated k-means++. 9640-9649
		Zhenan Fan, Xinglu Wang, Oleksandr Yakovenko, Abdullah Ali Sivas, Owen Ren, Yong Zhang, Zirui Zhou:
Smart Initial Basis Selection for Linear Programs. 9650-9664
		Vladimir Fanaskov, Tianchi Yu, Alexander Rudikov, Ivan V. Oseledets:
General Covariance Data Augmentation for Neural PDE Solvers. 9665-9688
		Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen:
The Fast Johnson-Lindenstrauss Transform Is Even Faster. 9689-9715
		Guanhua Fang, Ping Li:
Regression with Label Permutation in Generalized Linear Model. 9716-9760
		Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
Robust Collaborative Learning with Linear Gradient Overhead. 9761-9813
		Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Neural FIM for learning Fisher information metrics from point cloud data. 9814-9826
		Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. 9827-9869
		Jonathan Feldstein, Modestas Jurcius, Efthymia Tsamoura:
Parallel Neurosymbolic Integration with Concordia. 9870-9885
		Mattie Fellows, Matthew J. A. Smith, Shimon Whiteson:
Why Target Networks Stabilise Temporal Difference Methods. 9886-9909
		Dieqiao Feng, Yuanqi Du, Carla P. Gomes, Bart Selman:
Weighted Sampling without Replacement for Deep Top-k Classification. 9910-9920
		Zhe Feng, Christopher Liaw, Zixin Zhou:
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. 9921-9937
		Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma:
Fractional Denoising for 3D Molecular Pre-training. 9938-9961
		Ying Feng, David P. Woodruff:
Improved Algorithms for White-Box Adversarial Streams. 9962-9975
		Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. 9976-10007
		Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. 10008-10033
		Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. 10034-10052
		Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat:
Scaling Laws for Multilingual Neural Machine Translation. 10053-10071
		Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay:
Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation. 10072-10092
		Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko:
Adapting to game trees in zero-sum imperfect information games. 10093-10135
		Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. 10136-10152
		Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna M. Wardlaw, Grant Mair, Emanuele Trucco, Amos J. Storkey:
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. 10153-10169
		Alexandre Forel, Axel Parmentier, Thibaut Vidal:
Explainable Data-Driven Optimization: From Context to Decision and Back Again. 10170-10187
		Dylan J. Foster, Noah Golowich, Sham M. Kakade:
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. 10188-10221
		Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D. Cantwell, Anil Anthony Bharath:
Disentangled Generative Models for Robust Prediction of System Dynamics. 10222-10248
		Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? 10249-10264
		Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi:
Last Switch Dependent Bandits with Monotone Payoff Functions. 10265-10284
		Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi:
A Theoretical Analysis of the Learning Dynamics under Class Imbalance. 10285-10322
		Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. 10323-10337
		Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset:
Learning Temporally AbstractWorld Models without Online Experimentation. 10338-10356
		Gideon Joseph Freund, Elad Sarafian, Sarit Kraus:
A Coupled Flow Approach to Imitation Learning. 10357-10372
		Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré:
Simple Hardware-Efficient Long Convolutions for Sequence Modeling. 10373-10391
		Yang Fu, Ishan Misra, Xiaolong Wang:
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses. 10392-10404
		Yao Fu, Run Peng, Honglak Lee:
Go Beyond Imagination: Maximizing Episodic Reachability with World Models. 10405-10420
		Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot:
Specializing Smaller Language Models towards Multi-Step Reasoning. 10421-10430
		Qiang Fu, Dongchu Xu, Ashia Camage Wilson:
Accelerated Stochastic Optimization Methods under Quasar-convexity. 10431-10460
		Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris:
Meta-learning Parameterized Skills. 10461-10481
		Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan Celine Lin:
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations. 10482-10493
		Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo:
Hierarchies of Reward Machines. 10494-10541
		Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz:
Why Random Pruning Is All We Need to Start Sparse. 10542-10570
		Quentin Gallouédec, Emmanuel Dellandréa:
Cell-Free Latent Go-Explore. 10571-10586
		Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira:
Graph Reinforcement Learning for Network Control via Bi-Level Optimization. 10587-10610
		Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? 10611-10627
		Roy Ganz, Bahjat Kawar, Michael Elad:
Do Perceptually Aligned Gradients Imply Robustness? 10628-10648
		Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye:
Solving Linear Programs with Fast Online Learning Algorithms. 10649-10675
		Yihang Gao, Yiqi Gu, Michael Ng:
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks. 10676-10707
		Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. 10708-10726
		Ruijiang Gao, Himabindu Lakkaraju:
On the Impact of Algorithmic Recourse on Social Segregation. 10727-10743
		Rui Gao, Weiwei Liu:
DDGR: Continual Learning with Deep Diffusion-based Generative Replay. 10744-10763
		Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig:
PAL: Program-aided Language Models. 10764-10799
		Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang:
Out-of-Domain Robustness via Targeted Augmentations. 10800-10834
		Leo Gao, John Schulman, Jacob Hilton:
Scaling Laws for Reward Model Overoptimization. 10835-10866
		Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat:
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation. 10867-10878
		Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. 10879-10928
		Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun:
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. 10929-10974
		Quentin Garrido, Laurent Najman, Yann LeCun:
Self-supervised learning of Split Invariant Equivariant representations. 10975-10996
		Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh:
Federated Heavy Hitter Recovery under Linear Sketching. 10997-11012
		Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal:
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization. 11013-11049
		Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song:
A Reinforcement Learning Framework for Dynamic Mediation Analysis. 11050-11097
		Tomas Geffner, George Papamakarios, Andriy Mnih:
Compositional Score Modeling for Simulation-Based Inference. 11098-11116
		Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a single GPU in one day. 11117-11143
		Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. 11144-11172
		Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. 11173-11195
		Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si:
Towards Reliable Neural Specifications. 11196-11212
		Matthias Gerstgrasser, David C. Parkes:
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. 11213-11236
		Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. 11237-11254
		Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. 11255-11282
		Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. 11283-11299
		Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. 11300-11320
		Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine:
Reinforcement Learning from Passive Data via Latent Intentions. 11321-11339
		Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada, Chul Lee, Seong-Hyok Sean Kim, Hyukgeun Cha, Yunjun Choi, Dongho Kim, Jeong-Il Kye, Vincent Emanuel Elfving:
Harmonic Neural Networks. 11340-11359
		Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich:
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat. 11360-11397
		Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos:
Looped Transformers as Programmable Computers. 11398-11442
		Luca Giuliani, Eleonora Misino, Michele Lombardi:
Generalized Disparate Impact for Configurable Fairness Solutions in ML. 11443-11458
		Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. 11459-11492
		Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Adversarial robustness of amortized Bayesian inference. 11493-11524
		Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak:
Efficient RL via Disentangled Environment and Agent Representations. 11525-11545
		Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman:
Aligning Language Models with Preferences through f-divergence Minimization. 11546-11583
		Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon:
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. 11584-11597
		Weiyuan Gong, Scott Aaronson:
Learning Distributions over Quantum Measurement Outcomes. 11598-11613
		Eduard Gorbunov, Adrien B. Taylor, Samuel Horváth, Gauthier Gidel:
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. 11614-11641
		Shirin Goshtasbpour, Victor Cohen, Fernando Pérez-Cruz:
Adaptive Annealed Importance Sampling with Constant Rate Progress. 11642-11658
		Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Formalizing Preferences Over Runtime Distributions. 11659-11682
		Vincent Peter Grande, Michael T. Schaub:
Topological Point Cloud Clustering. 11683-11697
		Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. 11698-11733
		J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick:
Hidden Symmetries of ReLU Networks. 11734-11760
		Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik:
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. 11761-11807
		Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M. Susskind, Christian Theobalt, Lingjie Liu, Ravi Ramamoorthi:
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion. 11808-11826
		Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu:
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. 11827-11846
		Aritra Guha, Nhat Ho, XuanLong Nguyen:
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances. 11847-11870
		Etash Kumar Guha, Eugène Ndiaye, Xiaoming Huo:
Conformalization of Sparse Generalized Linear Models. 11871-11887
		Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat:
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. 11888-11904
		Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang:
Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. 11905-11933
		Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang:
FeDXL: Provable Federated Learning for Deep X-Risk Optimization. 11934-11966
		Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. 11967-11997
		Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi:
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. 11998-12011
		Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao:
Linkless Link Prediction via Relational Distillation. 12012-12033
		Yongxin Guo, Xiaoying Tang, Tao Lin:
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. 12034-12054
		Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik:
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction. 12055-12076
		Yuhe Guo, Zhewei Wei:
Graph Neural Networks with Learnable and Optimal Polynomial Bases. 12077-12097
		Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian J. McAuley:
LongCoder: A Long-Range Pre-trained Language Model for Code Completion. 12098-12107
		Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long:
Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms. 12108-12121
		Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li, Zhi-Hua Zhou:
Identifying Useful Learnwares for Heterogeneous Label Spaces. 12122-12131
		Shivam Gupta, Jasper C. H. Lee, Eric Price:
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors. 12132-12164
		Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur:
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets. 12165-12181
		Chirag Gupta, Aaditya Ramdas:
Online Platt Scaling with Calibeating. 12182-12204
		NareshKumar Gurulingan, Bahram Zonooz, Elahe Arani:
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal. 12205-12223
		Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat:
Conditionally Strongly Log-Concave Generative Models. 12224-12251
		Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. 12252-12267
		Marie Guyomard, Susana Barbosa, Lionel Fillatre:
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network. 12268-12291
		Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski:
Conformal Prediction Sets for Graph Neural Networks. 12292-12318
		Seungwoong Ha, Hawoong Jeong:
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning. 12319-12338
		Daniel Haider, Martin Ehler, Péter Balázs:
Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction. 12339-12350
		Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta:
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. 12351-12367
		Boran Han:
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition. 12368-12388
		Hyeongrok Han, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon:
On the Impact of Knowledge Distillation for Model Interpretability. 12389-12410
		Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang:
Alternately Optimized Graph Neural Networks. 12411-12429
		Yena Han, Tomaso A. Poggio, Brian Cheung:
System Identification of Neural Systems: If We Got It Right, Would We Know? 12430-12444
		Jonas Berg Hansen, Filippo Maria Bianchi:
Total Variation Graph Neural Networks. 12445-12468
		Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. 12469-12510
		Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang:
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. 12511-12526
		Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen:
Leveraging Demonstrations to Improve Online Learning: Quality Matters. 12527-12545
		Xiaoran Hao, Patrick Shafto:
Coupled Variational Autoencoder. 12546-12555
		Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu:
GNOT: A General Neural Operator Transformer for Operator Learning. 12556-12569
		Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. 12570-12586
		Marc Härkönen, Markus Lange-Hegermann, Bogdan Raita:
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. 12587-12615
		Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. 12616-12632
		Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov:
Global Context Vision Transformers. 12633-12646
		Martin B. Haugh, Raghav Singal:
Counterfactual Analysis in Dynamic Latent State Models. 12647-12677
		Satoshi Hayakawa, Harald Oberhauser, Terry J. Lyons:
Sampling-based Nyström Approximation and Kernel Quadrature. 12678-12699
		Soufiane Hayou, Greg Yang:
Width and Depth Limits Commute in Residual Networks. 12700-12723
		Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson:
A Generalization of ViT/MLP-Mixer to Graphs. 12724-12745
		Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik:
Domain Adaptation for Time Series Under Feature and Label Shifts. 12746-12774
		Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin, Yuxiao Huang, Zhen Wang, Weixiong Zhang:
Contrastive Learning Meets Homophily: Two Birds with One Stone. 12775-12789
		Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. 12790-12822
		S. Ashwin Hebbar, Viraj Vivek Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath:
CRISP: Curriculum based Sequential neural decoders for Polar code family. 12823-12845
		Jonathan Hehir, Daniel Ting, Graham Cormode:
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting. 12846-12865
		Florian Heinrichs, Mavin Heim, Corinna Weber:
Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. 12866-12881
		Joey Hejna, Jensen Gao, Dorsa Sadigh:
Distance Weighted Supervised Learning for Offline Interaction Data. 12882-12906
		Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji:
Group Equivariant Fourier Neural Operators for Partial Differential Equations. 12907-12930
		Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low:
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. 12931-12971
		Mikael Henaff, Minqi Jiang, Roberta Raileanu:
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs. 12972-12999
		Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim:
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding. 13000-13016
		Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz:
Generalized Teacher Forcing for Learning Chaotic Dynamics. 13017-13049
		Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen:
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. 13050-13084
		Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. 13085-13117
		Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert:
AdaBoost is not an Optimal Weak to Strong Learner. 13118-13140
		Rasmus Kjær Høier, D. Staudt, Christopher Zach:
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons. 13141-13156
		Joey Hong, Branislav Kveton, Manzil Zaheer, Sumeet Katariya, Mohammad Ghavamzadeh:
Multi-Task Off-Policy Learning from Bandit Feedback. 13157-13173
		Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. 13174-13198
		Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, Jiayu Zhou:
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers. 13199-13212
		Emiel Hoogeboom, Jonathan Heek, Tim Salimans:
simple diffusion: End-to-end diffusion for high resolution images. 13213-13232
		Guy Horowitz, Nir Rosenfeld:
Causal Strategic Classification: A Tale of Two Shifts. 13233-13253
		Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie:
Fair and Accurate Decision Making through Group-Aware Learning. 13254-13269
		Sèdjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian:
Approximation Algorithms for Fair Range Clustering. 13270-13284
		Elizabeth Mary Hou, Gregory David Castañón:
Decoding Layer Saliency in Language Transformers. 13285-13308
		Bairu Hou, Joe O'Connor, Jacob Andreas, Shiyu Chang, Yang Zhang:
PromptBoosting: Black-Box Text Classification with Ten Forward Passes. 13309-13324
		Boya Hou, Sina Sanjari, Nathan Dahlin, Subhonmesh Bose, Umesh Vaidya:
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach. 13325-13352
		Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong:
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits. 13353-13409
		Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro:
Automatic Data Augmentation via Invariance-Constrained Learning. 13410-13433
		Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum:
Thompson Sampling with Diffusion Generative Prior. 13434-13468
		Zhengmian Hu, Heng Huang:
Tighter Analysis for ProxSkip. 13469-13496
		Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang:
Omnipredictors for Constrained Optimization. 13497-13527
		Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio:
GFlowNet-EM for Learning Compositional Latent Variable Models. 13528-13549
		Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. 13550-13583
		Hengyuan Hu, Dorsa Sadigh:
Language Instructed Reinforcement Learning for Human-AI Coordination. 13584-13598
		Yuan-Ting Hu, Alexander G. Schwing, Raymond A. Yeh:
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction. 13599-13609
		Zixuan Hu, Li Shen, Zhenyi Wang, Baoyuan Wu, Chun Yuan, Dacheng Tao:
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning. 13610-13627
		Yingdong Hu, Renhao Wang, Li Erran Li, Yang Gao:
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal. 13628-13651
		Zhengmian Hu, Xidong Wu, Heng Huang:
Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization. 13652-13678
		Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao:
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. 13679-13709
		Audrey Huang, Jinglin Chen, Nan Jiang:
Reinforcement Learning in Low-rank MDPs with Density Features. 13710-13752
		Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, Jingren Zhou:
Composer: Creative and Controllable Image Synthesis with Composable Conditions. 13753-13773
		Zizheng Huang, Haoxing Chen, Ziqi Wen, Chao Zhang, Huaxiong Li, Bo Wang, Chunlin Chen:
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas. 13774-13790
		Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang:
High-dimensional Clustering onto Hamiltonian Cycle. 13791-13813
		Jiatai Huang, Yan Dai, Longbo Huang:
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning. 13814-13844
		Junyu Huang, Qilong Feng, Ziyun Huang, Jinhui Xu, Jianxin Wang:
Fast Algorithms for Distributed k-Clustering with Outliers. 13845-13868
		Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. 13869-13890
		Lingxiao Huang, Ruiyuan Huang, Zengfeng Huang, Xuan Wu:
On Coresets for Clustering in Small Dimensional Euclidean spaces. 13891-13915
		Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, Zhou Zhao:
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. 13916-13932
		Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou:
The Power of Uniform Sampling for k-Median. 13933-13956
		Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su:
Reparameterized Policy Learning for Multimodal Trajectory Optimization. 13957-13975
		Yufan Huang, C. Seshadhri, David F. Gleich:
Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming. 13976-13992
		Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu:
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition. 13993-14006
		Jialei Huang, Zhao-Heng Yin, Yingdong Hu, Yang Gao:
Policy Contrastive Imitation Learning. 14007-14022
		Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? 14023-14038
		Minhui Huang, Dewei Zhang, Kaiyi Ji:
Achieving Linear Speedup in Non-IID Federated Bilevel Learning. 14039-14059
		Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang:
Federated Linear Contextual Bandits with User-level Differential Privacy. 14060-14095
		Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. 14096-14113
		Like Hui, Mikhail Belkin, Stephen Wright:
Cut your Losses with Squentropy. 14114-14131
		Iris A. M. Huijben, Arthur Andreas Nijdam, Sebastiaan Overeem, Merel M. van Gilst, Ruud van Sloun:
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series. 14132-14152
		Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot:
One-Shot Federated Conformal Prediction. 14153-14177
		Aamal Abbas Hussain, Francesco Belardinelli, Dario Paccagnan:
The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics. 14178-14202
		Taehyun Hwang, Kyuwook Chai, Min Hwan Oh:
Combinatorial Neural Bandits. 14203-14236
		Geonho Hwang, Jaewoong Choi, Hyunsoo Cho, Myungjoo Kang:
MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action. 14237-14248
		HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim:
Information-Theoretic State Space Model for Multi-View Reinforcement Learning. 14249-14282
		Shahana Ibrahim, Xiao Fu, Rebecca A. Hutchinson, Eugene Seo:
Under-Counted Tensor Completion with Neural Incorporation of Attributes. 14283-14315
		Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. 14316-14332
		Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. 14333-14352
		Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. 14353-14375
		Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv:
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods. 14376-14389
		Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Principled Offline RL in the Presence of Rich Exogenous Information. 14390-14421
		Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard:
Unveiling the Latent Space Geometry of Push-Forward Generative Models. 14422-14444
		Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. 14445-14464
		Maor Ivgi, Oliver Hinder, Yair Carmon:
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. 14465-14499
		Gaurav Iyer, Boris Hanin, David Rolnick:
Maximal Initial Learning Rates in Deep ReLU Networks. 14500-14530
		Zachary Izzo, Ruishan Liu, James Zou:
Data-Driven Subgroup Identification for Linear Regression. 14531-14552
		Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar:
Efficient Training of Language Models using Few-Shot Learning. 14553-14568
		Allan Jabri, David J. Fleet, Ting Chen:
Scalable Adaptive Computation for Iterative Generation. 14569-14589
		Andrew Jacobsen, Ashok Cutkosky:
Unconstrained Online Learning with Unbounded Losses. 14590-14630
		Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio:
Multi-Objective GFlowNets. 14631-14653
		Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith:
The Price of Differential Privacy under Continual Observation. 14654-14678
		Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang:
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication. 14679-14690
		Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang:
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models. 14691-14701
		Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, Minjoon Seo:
Exploring the Benefits of Training Expert Language Models over Instruction Tuning. 14702-14729
		Jinhyeok Jang, Woo-han Yun, Won Hwa Kim, Youngwoo Yoon, Jaehong Kim, Jaeyeon Lee, ByungOk Han:
Learning to Boost Training by Periodic Nowcasting Near Future Weights. 14730-14757
		Faris Janjos, Lars Rosenbaum, Maxim Dolgov, J. Marius Zoellner:
Unscented Autoencoder. 14758-14779
		Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko:
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. 14780-14816
		Kishaan Jeeveswaran, Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani:
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning. 14817-14835
		Hyeonsu Jeong, Hye Won Chung:
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing. 14836-14868
		Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao, Jielong Yang, Wee Peng Tay:
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks. 14869-14885
		Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yuke Xiang, Yu-Feng Li:
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. 14886-14901
		Su Jia, Nishant Oli, Ian Anderson, Paul Duff, Andrew A. Li, R. Ravi:
Short-lived High-volume Bandits. 14902-14929
		Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier:
Smooth Non-stationary Bandits. 14930-14944
		Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu:
A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. 14945-14974
		Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan:
VIMA: Robot Manipulation with Multimodal Prompts. 14975-15022
		Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson:
Estimating Causal Effects using a Multi-task Deep Ensemble. 15023-15040
		Bowen Jiang, Bo Jiang, Jian Li, Tao Lin, Xinbing Wang, Chenghu Zhou:
Online Restless Bandits with Unobserved States. 15041-15066
		Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. 15067-15088
		Yulun Jiang, Chen Liu, Zhichao Huang, Mathieu Salzmann, Sabine Süsstrunk:
Towards Stable and Efficient Adversarial Training against l1 Bounded Adversarial Attacks. 15089-15104
		Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. 15105-15124
		Ziwei Jiang, Lai Wei, Murat Kocaoglu:
Approximate Causal Effect Identification under Weak Confounding. 15125-15143
		Guangyuan Jiang, Manjie Xu, Shiji Xin, Wei Liang, Yujia Peng, Chi Zhang, Yixin Zhu:
MEWL: Few-shot multimodal word learning with referential uncertainty. 15144-15169
		Chenbo Jiang, Jie Yang, Shwai He, Yu-Kun Lai, Lin Gao:
NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes. 15170-15185
		Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. 15186-15199
		Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. 15200-15238
		Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu:
Thompson Sampling with Less Exploration is Fast and Optimal. 15239-15261
		Daniel D. Johnson, Daniel Tarlow, Christian Walder:
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. 15262-15306
		Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt:
Automatically Auditing Large Language Models via Discrete Optimization. 15307-15329
		Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio:
On the Expressive Power of Geometric Graph Neural Networks. 15330-15355
		Siddharth Joshi, Baharan Mirzasoleiman:
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least. 15356-15370
		Kishor Jothimurugan, Steve Hsu, Osbert Bastani, Rajeev Alur:
Robust Subtask Learning for Compositional Generalization. 15371-15387
		Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. 15388-15400
		Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning with Practical Certificates. 15401-15420
		Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi:
Scaling of Class-wise Training Losses for Post-hoc Calibration. 15421-15434
		Yeonsung Jung, Hajin Shim, June Yong Yang, Eunho Yang:
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation. 15435-15450
		Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Joint Treatment Effects by Combining Multiple Experiments. 15451-15527
		Mateusz Maria Jurewicz, Graham W. Taylor, Leon Derczynski:
The Catalog Problem: Clustering and Ordering Variable-Sized Sets. 15528-15545
		Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh:
Equivariance with Learned Canonicalization Functions. 15546-15566
		Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami:
Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies. 15567-15585
		Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Statistical Indistinguishability of Learning Algorithms. 15586-15622
		Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. 15623-15638
		David Kaltenpoth, Jilles Vreeken:
Nonlinear Causal Discovery with Latent Confounders. 15639-15654
		Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin:
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. 15655-15668
		Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida:
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. 15669-15695
		Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel:
Large Language Models Struggle to Learn Long-Tail Knowledge. 15696-15707
		Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans, Vishal Baskaran, Tenghao Huang, Haokun Liu, Colin Raffel:
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. 15708-15719
		Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde, Joseph Teran:
A Deep Conjugate Direction Method for Iteratively Solving Linear Systems. 15720-15736
		Juwon Kang, Nayeong Kim, Donghyeon Kwon, Jungseul Ok, Suha Kwak:
Leveraging Proxy of Training Data for Test-Time Adaptation. 15737-15752
		Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, Donglin Wang:
Beyond Reward: Offline Preference-guided Policy Optimization. 15753-15768
		Siteng Kang, Zhan Shi, Xinhua Zhang:
Poisoning Generative Replay in Continual Learning to Promote Forgetting. 15769-15785
		Qiyu Kang, Kai Zhao, Yang Song, Sijie Wang, Wee Peng Tay:
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks. 15786-15808
		Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. 15809-15827
		Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra:
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. 15828-15860
		Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. 15861-15883
		Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee:
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. 15884-15899
		Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
General Sequential Episodic Memory Model. 15900-15910
		Takayuki Katsuki, Takayuki Osogami:
Regression with Sensor Data Containing Incomplete Observations. 15911-15927
		Ilya Kaufman, Omri Azencot:
Data Representations' Study of Latent Image Manifolds. 15928-15945
		Prannay Kaul, Weidi Xie, Andrew Zisserman:
Multi-Modal Classifiers for Open-Vocabulary Object Detection. 15946-15969
		Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Learning Mixtures of Markov Chains and MDPs. 15970-16017
		Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber:
Curious Replay for Model-based Adaptation. 16018-16048
		Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? 16049-16096
		Yuta Kawakami, Manabu Kuroki, Jin Tian:
Instrumental Variable Estimation of Average Partial Causal Effects. 16097-16130
		Zeki Kazan, Kaiyan Shi, Adam Groce, Andrew P. Bray:
The Test of Tests: A Framework for Differentially Private Hypothesis Testing. 16131-16151
		Chuyang Ke, Jean Honorio:
Exact Inference in High-order Structured Prediction. 16152-16167
		T. Anderson Keller, Max Welling:
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. 16168-16189
		Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. 16190-16215
		Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry:
Rethinking Backdoor Attacks. 16216-16236
		Adam Khakhar, Stephen Mell, Osbert Bastani:
PAC Prediction Sets for Large Language Models of Code. 16237-16249
		Mohammad Khalafi, Digvijay Boob:
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems. 16250-16270
		Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan:
Loss Balancing for Fair Supervised Learning. 16271-16290
		Prashant Khanduri, Ioannis C. Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong:
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. 16291-16325
		Mahyar Khayatkhoei, Wael Abd-Almageed:
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions. 16326-16343
		Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii:
Learning-augmented private algorithms for multiple quantile release. 16344-16376
		Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien:
CrossSplit: Mitigating Label Noise Memorization through Data Splitting. 16377-16392
		Timothy Doyeon Kim, Tankut Can, Kamesh Krishnamurthy:
Trainability, Expressivity and Interpretability in Gated Neural ODEs. 16393-16423
		Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon:
SAAL: Sharpness-Aware Active Learning. 16424-16440
		Jigang Kim, Daesol Cho, H. Jin Kim:
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum. 16441-16457
		Wonyoung Kim, Garud Iyengar, Assaf Zeevi:
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback. 16458-16501
		Jinuk Kim, Yeonwoo Jeong, Deokjae Lee, Hyun Oh Song:
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming. 16502-16520
		Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, Sungroh Yoon:
Probabilistic Concept Bottleneck Models. 16521-16540
		Haeyeon Kim, Minsu Kim, Federico Berto, Joungho Kim, Jinkyoo Park:
DevFormer: A Symmetric Transformer for Context-Aware Device Placement. 16541-16566
		Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon:
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models. 16567-16598
		Junghoon Kim, Taejoon Kim, David J. Love, Christopher G. Brinton:
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning. 16599-16618
		Woojun Kim, Jeonghye Kim, Youngchul Sung:
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework. 16619-16638
		Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun:
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models. 16639-16653
		Seunghyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee, Juho Lee:
Probabilistic Imputation for Time-series Classification with Missing Data. 16654-16667
		Seongun Kim, Kyowoon Lee, Jaesik Choi:
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. 16668-16695
		Byungjoo Kim, Suyoung Lee, Seanie Lee, Sooel Son, Sung Ju Hwang:
Margin-based Neural Network Watermarking. 16696-16711
		Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee:
Regularizing Towards Soft Equivariance Under Mixed Symmetries. 16712-16727
		Byeongchan Kim, Min Hwan Oh:
Model-based Offline Reinforcement Learning with Count-based Conservatism. 16728-16746
		Chanyeong Kim, Jongwoong Park, Hyunglip Bae, Woo Chang Kim:
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization. 16747-16770
		Junetae Kim, Kyoungsuk Park, Hanseok Jeong, Youngwook Kim, Jeongseon Kim, Sun-Young Kim:
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes. 16771-16786
		Seungwook Kim, Chunghyun Park, Yoonwoo Jeong, Jaesik Park, Minsu Cho:
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning. 16787-16806
		Jaehyung Kim, Jinwoo Shin, Dongyeop Kang:
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning. 16807-16828
		Woojun Kim, Youngchul Sung:
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning. 16829-16852
		Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. 16853-16876
		Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Bing Liu:
Learnability and Algorithm for Continual Learning. 16877-16896
		Jungbin Kim, Insoon Yang:
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions. 16897-16954
		Beomsu Kim, Jong Chul Ye:
Denoising MCMC for Accelerating Diffusion-Based Generative Models. 16955-16977
		Dale Kim, Qing Zhou:
Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs. 16978-16996
		Kwangho Kim, José R. Zubizarreta:
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning. 16997-17014
		Dhamma Kimpara, Rafael M. Frongillo, Bo Waggoner:
Proper Losses for Discrete Generative Models. 17015-17040
		Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda:
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. 17041-17060
		John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein:
A Watermark for Large Language Models. 17061-17084
		Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh:
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs. 17085-17104
		Matthias Kirchler, Christoph Lippert, Marius Kloft:
Training Normalizing Flows from Dependent Data. 17105-17121
		Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q. Weinberger:
IncDSI: Incrementally Updatable Document Retrieval. 17122-17134
		Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. 17135-17175
		Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh:
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. 17176-17197
		Martin Klissarov, Marlos C. Machado:
Deep Laplacian-based Options for Temporally-Extended Exploration. 17198-17217
		Marina Knittel, Max Springer, John P. Dickerson, MohammadTaghi Hajiaghayi:
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost. 17218-17242
		Boris Knyazev, Doha Hwang, Simon Lacoste-Julien:
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? 17243-17259
		Tomás Kocák, Alexandra Carpentier:
Online Learning with Feedback Graphs: The True Shape of Regret. 17260-17282
		Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried:
Grounding Language Models to Images for Multimodal Inputs and Outputs. 17283-17300
		Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé:
Rigid Body Flows for Sampling Molecular Crystal Structures. 17301-17326
		Nils Kohring, Fabian Raoul Pieroth, Martin Bichler:
Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets. 17327-17342
		Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich:
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. 17343-17363
		Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge:
On Computing Optimal Tree Ensembles. 17364-17374
		Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. 17375-17390
		Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang:
Autoregressive Diffusion Model for Graph Generation. 17391-17408
		Xiangzhe Kong, Wenbing Huang, Yang Liu:
End-to-End Full-Atom Antibody Design. 17409-17429
		Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim:
Covariate balancing using the integral probability metric for causal inference. 17430-17461
		Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim:
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. 17462-17491
		Tatsuya Konishi, Mori Kurokawa, Chihiro Ono, Zixuan Ke, Gyuhak Kim, Bing Liu:
Parameter-Level Soft-Masking for Continual Learning. 17492-17505
		Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez:
Pretraining Language Models with Human Preferences. 17506-17533
		Ezgi Korkmaz, Jonah Brown-Cohen:
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. 17534-17543
		Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. 17544-17563
		Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko:
TabDDPM: Modelling Tabular Data with Diffusion Models. 17564-17579
		Vignesh Kothapalli:
Randomized Schur Complement Views for Graph Contrastive Learning. 17580-17614
		Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu:
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. 17615-17659
		Batuhan Koyuncu, Pablo Sánchez-Martín, Ignacio Peis, Pablo M. Olmos, Isabel Valera:
Variational Mixture of HyperGenerators for Learning Distributions over Functions. 17660-17683
		Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon:
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. 17684-17744
		Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. 17745-17783
		Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang:
Multi-Task Differential Privacy Under Distribution Skew. 17784-17807
		Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju:
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. 17808-17826
		Sanjukta Krishnagopal, Luana Ruiz:
Graph Neural Tangent Kernel: Convergence on Large Graphs. 17827-17841
		Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover:
Diffusion Models for Black-Box Optimization. 17842-17857
		Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves, Tongkai Liu, Hiba Ajmal, Hamidreza Aghasi, Roy Fox:
Learning to Design Analog Circuits to Meet Threshold Specifications. 17858-17873
		Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou:
Variance Control for Distributional Reinforcement Learning. 17874-17895
		Kalle Kujanpää, Joni Pajarinen, Alexander Ilin:
Hierarchical Imitation Learning with Vector Quantized Models. 17896-17919
		Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli:
SinDDM: A Single Image Denoising Diffusion Model. 17920-17930
		Sean Kulinski, David I. Inouye:
Towards Explaining Distribution Shifts. 17931-17952
		Manoj Kumar, Anurag Sharma, Shashwat Saxena, Sandeep Kumar:
Featured Graph Coarsening with Similarity Guarantees. 17953-17975
		Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal, Emily Jin, Chengshu Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu, Silvio Savarese, Roberto Martín-Martín:
Modeling Dynamic Environments with Scene Graph Memory. 17976-17993
		Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk:
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. 17994-18007
		Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Víctor Elvira, Jens Lagergren:
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders. 18008-18022
		Minseop Kwak, Jiuhn Song, Seungryong Kim:
GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency. 18023-18036
		Sehyun Kwon, Joo Young Choi, Ernest K. Ryu:
Rotation and Translation Invariant Representation Learning with Implicit Neural Representations. 18037-18056
		Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Reward-Mixing MDPs with Few Latent Contexts are Learnable. 18057-18082
		Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak:
A Fully First-Order Method for Stochastic Bilevel Optimization. 18083-18113
		Dohyun Kwon, Hanbaek Lyu:
Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning. 18114-18134
		Yongchan Kwon, James Zou:
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. 18135-18152
		Aqeel Labash, Florian Stelzer, Daniel Majoral, Raul Vicente Zafra:
Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning. 18153-18170
		Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand:
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. 18171-18206
		Steinar Laenen, Bogdan-Adrian Manghiuc, He Sun:
Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs. 18207-18249
		Marc Lafon, Elias Ramzi, Clément Rambour, Nicolas Thome:
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection. 18250-18268
		Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin:
A theory of continuous generative flow networks. 18269-18300
		Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon:
Automatically marginalized MCMC in probabilistic programming. 18301-18318
		Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-Tau Yih, Daniel Fried, Sida I. Wang, Tao Yu:
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. 18319-18345
		Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo:
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer. 18346-18364
		Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation. 18365-18398
		Clément Lalanne, Aurélien Garivier, Rémi Gribonval:
Private Statistical Estimation of Many Quantiles. 18399-18418
		Henry Lam, Zhenyuan Liu:
Bootstrap in High Dimension with Low Computation. 18419-18453
		Kevin H. Lam, Christian J. Walder, Spiridon Penev, Richard Nock:
LegendreTron: Uprising Proper Multiclass Loss Learning. 18454-18470
		Andre Lamurias, Alessandro Tibo, Katja Hose, Mads Albertsen, Thomas Dyhre Nielsen:
Metagenomic Binning using Connectivity-constrained Variational Autoencoders. 18471-18481
		Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov:
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback. 18482-18534
		Robert Tjarko Lange, Henning Sprekeler:
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability. 18535-18547
		Romain Laroche, Remi Tachet des Combes:
On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs. 18548-18562
		Alexandra Anna Lassota, Alexander Lindermayr, Nicole Megow, Jens Schlöter:
Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints. 18563-18583
		Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii:
Speeding Up Bellman Ford via Minimum Violation Permutations. 18584-18598
		Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D. Dennis, Stuart Russell:
Who Needs to Know? Minimal Knowledge for Optimal Coordination. 18599-18613
		Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux:
Target-based Surrogates for Stochastic Optimization. 18614-18651
		Connor Lawless, Oktay Günlük:
Cluster Explanation via Polyhedral Descriptions. 18652-18666
		Phuong-Hang Le, Hongyu Gong, Changhan Wang, Juan Pino, Benjamin Lecouteux, Didier Schwab:
Pre-training for Speech Translation: CTC Meets Optimal Transport. 18667-18685
		Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. 18686-18713
		Tosca Lechner, Ruth Urner, Shai Ben-David:
Strategic Classification with Unknown User Manipulations. 18714-18732
		Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. 18733-18773
		Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin:
Towards Deep Attention in Graph Neural Networks: Problems and Remedies. 18774-18795
		Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang:
InGram: Inductive Knowledge Graph Embedding via Relation Graphs. 18796-18809
		Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. 18810-18851
		Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, Chanyoung Park:
Conditional Graph Information Bottleneck for Molecular Relational Learning. 18852-18871
		Seul Lee, Jaehyeong Jo, Sung Ju Hwang:
Exploring Chemical Space with Score-based Out-of-distribution Generation. 18872-18892
		Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova:
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding. 18893-18912
		Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee:
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization. 18913-18939
		Chaejeong Lee, Jayoung Kim, Noseong Park:
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis. 18940-18956
		Sangyun Lee, Beomsu Kim, Jong Chul Ye:
Minimizing Trajectory Curvature of ODE-based Generative Models. 18957-18973
		Hangbin Lee, Youngjo Lee:
H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features. 18974-18987
		Hojoon Lee, Koanho Lee, Dongyoon Hwang, Hyunho Lee, Byungkun Lee, Jaegul Choo:
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning. 18988-19009
		Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee:
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption. 19010-19035
		Yoonjoo Lee, Kyungjae Lee, Sunghyun Park, Dasol Hwang, Jaehyeon Kim, Hong-In Lee, Moontae Lee:
QASA: Advanced Question Answering on Scientific Articles. 19036-19052
		Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani:
Demystifying Disagreement-on-the-Line in High Dimensions. 19053-19093
		Wonyeol Lee, Sejun Park, Alex Aiken:
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters. 19094-19140
		Sungyoon Lee, Jinseong Park, Jaewook Lee:
Implicit Jacobian regularization weighted with impurity of probability output. 19141-19184
		Sang-Hyun Lee, Seung-Woo Seo:
Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments. 19185-19199
		Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. 19200-19227
		Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar:
Learning Control by Iterative Inversion. 19228-19255
		Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault Levasseur:
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference. 19256-19273
		Yaniv Leviathan, Matan Kalman, Yossi Matias:
Fast Inference from Transformers via Speculative Decoding. 19274-19286
		Orin Levy, Alon Cohen, Asaf B. Cassel, Yishay Mansour:
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation. 19287-19314
		Dan Ley, Saumitra Mishra, Daniele Magazzeni:
GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations. 19315-19342
		Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu:
TIPS: Topologically Important Path Sampling for Anytime Neural Networks. 19343-19359
		Anqi Li, Byron Boots, Ching-An Cheng:
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. 19360-19384
		Alexander Cong Li, Ellis Langham Brown, Alexei A. Efros, Deepak Pathak:
Internet Explorer: Targeted Representation Learning on the Open Web. 19385-19406
		Yuxin Li, Wenchao Chen, Bo Chen, Dongsheng Wang, Long Tian, Mingyuan Zhou:
Prototype-oriented unsupervised anomaly detection for multivariate time series. 19407-19424
		Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik:
Learning Preconditioners for Conjugate Gradient PDE Solvers. 19425-19439
		Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal:
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. 19440-19459
		Li'ang Li, Yifei Duan, Guanghua Ji, Yongqiang Cai:
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation. 19460-19470
		Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li, Yang Liu:
FAIRER: Fairness as Decision Rationale Alignment. 19471-19489
		Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Xian Fu:
RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. 19490-19503
		Qinbin Li, Bingsheng He, Dawn Song:
Adversarial Collaborative Learning on Non-IID Features. 19504-19526
		Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. 19527-19564
		Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak:
Transformers as Algorithms: Generalization and Stability in In-context Learning. 19565-19594
		Rui Li, S. T. John, Arno Solin:
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. 19595-19615
		Shouheng Li, Dongwoo Kim, Qing Wang:
Local Vertex Colouring Graph Neural Networks. 19616-19637
		Xiaoyun Li, Ping Li:
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. 19638-19688
		Yuchen Li, Yuanzhi Li, Andrej Risteski:
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding. 19689-19729
		Junnan Li, Dongxu Li, Silvio Savarese, Steven C. H. Hoi:
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. 19730-19742
		Junfan Li, Shizhong Liao:
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression. 19743-19766
		Zexi Li, Tao Lin, Xinyi Shang, Chao Wu:
Revisiting Weighted Aggregation in Federated Learning with Neural Networks. 19767-19788
		Shaojie Li, Yong Liu:
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction. 19789-19810
		Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence Rates for Agnostic Nyström Kernel Learning. 19811-19836
		Yige Li, Xixiang Lyu, Xingjun Ma, Nodens Koren, Lingjuan Lyu, Bo Li, Yu-Gang Jiang:
Reconstructive Neuron Pruning for Backdoor Defense. 19837-19854
		Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. 19855-19881
		Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. 19882-19910
		Jiahang Li, Yakun Song, Xiang Song, David Wipf:
On the Initialization of Graph Neural Networks. 19911-19931
		Xiaoxiao Li, Zhao Song, Jiaming Yang:
Federated Adversarial Learning: A Framework with Convergence Analysis. 19932-19959
		Ming Li, Sho Sonoda, Feilong Cao, Yu Guang Wang, Jiye Liang:
How Powerful are Shallow Neural Networks with Bandlimited Random Weights? 19960-19981
		Xiantao Li, Chunhao Wang:
Efficient Quantum Algorithms for Quantum Optimal Control. 19982-19994
		Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang:
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling. 19995-20034
		Wenhao Li, Xiangfeng Wang, Bo Jin, Hongyuan Zha:
Hierarchical Diffusion for Offline Decision Making. 20035-20064
		Wanshan Li, Daren Wang, Alessandro Rinaldo:
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions. 20065-20148
		Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Stan Z. Li:
Architecture-Agnostic Masked Image Modeling - From ViT back to CNN. 20149-20167
		Peizhao Li, Ethan Xia, Hongfu Liu:
Learning Antidote Data to Individual Unfairness. 20168-20181
		Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui:
Propensity Matters: Measuring and Enhancing Balancing for Recommendation. 20182-20194
		Yuwen Li, Miao Xiong, Bryan Hooi:
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks. 20195-20209
		Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha:
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process. 20210-20220
		Shengshi Li, Lin Yang:
Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds. 20221-20252
		Weixin Li, Xiaodong Yang:
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving. 20253-20275
		Pengfei Li, Jianyi Yang, Shaolei Ren:
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees. 20276-20295
		Songze Li, Duanyi Yao, Jin Liu:
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. 20296-20311
		Jiayang Li, Jing Yu, Boyi Liu, Yu Nie, Zhaoran Wang:
Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints. 20312-20335
		Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation. 20336-20350
		Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. 20351-20383
		Chao Li, Junhua Zeng, Chunmei Li, Cesar F. Caiafa, Qibin Zhao:
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations. 20384-20411
		Qiyang Li, Yuexiang Zhai, Yi Ma, Sergey Levine:
Understanding the Complexity Gains of Single-Task RL with a Curriculum. 20412-20451
		Mingjie Li, Quanshi Zhang:
Does a Neural Network Really Encode Symbolic Concepts? 20452-20469
		Yang Li, Shao Zhang, Jichen Sun, Yali Du, Ying Wen, Xinbing Wang, Wei Pan:
Cooperative Open-ended Learning Framework for Zero-Shot Coordination. 20470-20484
		Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang:
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. 20485-20528
		Shaoang Li, Lan Zhang, Yingqi Yu, Xiangyang Li:
Optimal Arms Identification with Knapsacks. 20529-20555
		Mengdi Li, Xufeng Zhao, Jae Hee Lee, Cornelius Weber, Stefan Wermter:
Internally Rewarded Reinforcement Learning. 20556-20574
		Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu, Peng Wu:
Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. 20575-20598
		Shuangtong Li, Tianyi Zhou, Xinmei Tian, Dacheng Tao:
Structured Cooperative Learning with Graphical Model Priors. 20599-20622
		Enming Liang, Minghua Chen, Steven H. Low:
Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set. 20623-20649
		Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu:
Consistency of Multiple Kernel Clustering. 20650-20676
		Hao Liang, Zhi-Quan Luo:
A Distribution Optimization Framework for Confidence Bounds of Risk Measures. 20677-20705
		Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou:
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. 20706-20724
		Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, Ping Luo:
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners. 20725-20745
		Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian:
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. 20746-20762
		Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan:
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. 20763-20786
		James Chenhao Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang:
CLUSTSEG: Clustering for Universal Segmentation. 20787-20809
		Ziyi Liang, Yanfei Zhou, Matteo Sesia:
Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping. 20810-20851
		Chen Liang, Simiao Zuo, Qingru Zhang, Pengcheng He, Weizhu Chen, Tuo Zhao:
Less is More: Task-aware Layer-wise Distillation for Language Model Compression. 20852-20867
		Luofeng Liao, Christian Kroer:
Statistical Inference and A/B Testing for First-Price Pacing Equilibria. 20868-20905
		Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization. 20906-20938
		Yun-Hsuan Lien, Ping-Chun Hsieh, Yu-Shuen Wang:
Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization. 20939-20949
		Valentin Liévin, Andreas Geert Motzfeldt, Ida Riis Jensen, Ole Winther:
Variational Open-Domain Question Answering. 20950-20977
		Yeqing Lin, Mohammed AlQuraishi:
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds. 20978-21002
		Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon:
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. 21003-21025
		Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt:
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. 21026-21050
		Zhenghao Lin, Yeyun Gong, Yelong Shen, Tong Wu, Zhihao Fan, Chen Lin, Nan Duan, Weizhu Chen:
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise. 21051-21064
		Weiwei Lin, Chenhang He, Man-Wai Mak, Youzhi Tu:
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations. 21065-21077
		Sen Lin, Peizhong Ju, Yingbin Liang, Ness B. Shroff:
Theory on Forgetting and Generalization of Continual Learning. 21078-21100
		Cheuk Yin Lin, Chaobing Song, Jelena Diakonikolas:
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization. 21101-21126
		Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang:
Safe Offline Reinforcement Learning with Real-Time Budget Constraints. 21127-21152
		Alexander Lin, Bahareh Tolooshams, Yves F. Atchadé, Demba E. Ba:
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. 21153-21181
		Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun:
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. 21182-21203
		Chieh Hubert Lin, Hung-Yu Tseng, Hsin-Ying Lee, Maneesh Kumar Singh, Ming-Hsuan Yang:
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features. 21204-21222
		Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Fair yet Asymptotically Equal Collaborative Learning. 21223-21259
		Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji:
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction. 21260-21287
		Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang:
Continuation Path Learning for Homotopy Optimization. 21288-21311
		Alexander Lindermayr, Nicole Megow, Martin Rapp:
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary. 21312-21334
		Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou:
Graph Mixup with Soft Alignments. 21335-21349
		Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Renhao Xue, James Song, Meikang Qiu, Liang Zhao:
Deep Graph Representation Learning and Optimization for Influence Maximization. 21350-21361
		Hao Liu, Pieter Abbeel:
Emergent Agentic Transformer from Chain of Hindsight Experience. 21362-21374
		Tommy Liu, Amanda S. Barnard:
Shapley Based Residual Decomposition for Instance Analysis. 21375-21387
		Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. 21388-21403
		Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen:
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. 21404-21425
		Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, HanQin Cai:
Towards Constituting Mathematical Structures for Learning to Optimize. 21426-21449
		Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo P. Mandic, Wenwu Wang, Mark D. Plumbley:
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. 21450-21474
		Yang Liu, Hao Cheng, Kun Zhang:
Identifiability of Label Noise Transition Matrix. 21475-21496
		Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang:
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining. 21497-21526
		Xing Liu, Andrew B. Duncan, Axel Gandy:
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. 21527-21547
		Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, Yang Cao:
Cones: Concept Neurons in Diffusion Models for Customized Generation. 21548-21566
		Weiming Liu, Haobo Fu, Qiang Fu, Wei Yang:
Opponent-Limited Online Search for Imperfect Information Games. 21567-21585
		Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, Ding Zhao:
Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data. 21586-21610
		Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao:
Constrained Decision Transformer for Offline Safe Reinforcement Learning. 21611-21630
		Liang Liu, Yanan Guo, Youtao Zhang, Jun Yang:
Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer. 21631-21657
		Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu:
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. 21658-21671
		Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng, Hung-Yi Lee, Shao-Hua Sun:
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs. 21672-21697
		Yi Liu, Qirui Hu, Lei Ding, Linglong Kong:
Online Local Differential Private Quantile Inference via Self-normalization. 21698-21714
		Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio:
GFlowOut: Dropout with Generative Flow Networks. 21715-21729
		Zhihong Liu, Hoang Anh Just, Xiangyu Chang, Xi Chen, Ruoxi Jia:
2D-Shapley: A Framework for Fragmented Data Valuation. 21730-21755
		Chenxi Liu, Kun Kuang:
Causal Structure Learning for Latent Intervened Non-stationary Data. 21756-21777
		Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li:
Structural Re-weighting Improves Graph Domain Adaptation. 21778-21793
		Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li:
Dink-Net: Neural Clustering on Large Graphs. 21794-21812
		Shih-Yang Liu, Zechun Liu, Kwang-Ting Cheng:
Oscillation-free Quantization for Low-bit Vision Transformers. 21813-21824
		Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang:
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits. 21825-21838
		Risheng Liu, Yaohua Liu, Wei Yao, Shangzhi Zeng, Jin Zhang:
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity. 21839-21866
		Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves:
Graph Switching Dynamical Systems. 21867-21883
		Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene, Huy Nguyen:
High Probability Convergence of Stochastic Gradient Methods. 21884-21914
		Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang:
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. 21915-21936
		Boyin Liu, Zhiqiang Pu, Yi Pan, Jianqiang Yi, Yanyan Liang, Du Zhang:
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning. 21937-21950
		Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu:
RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations. 21951-21968
		Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser:
Algorithms for bounding contribution for histogram estimation under user-level privacy. 21969-21996
		Evan Zheran Liu, Sahaana Suri, Tong Mu, Allan Zhou, Chelsea Finn:
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning. 21997-22008
		Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu:
Generating Private Synthetic Data with Genetic Algorithms. 22009-22027
		Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu:
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning. 22028-22041
		Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar:
I2SB: Image-to-Image Schrödinger Bridge. 22042-22062
		Fanghui Liu, Luca Viano, Volkan Cevher:
What can online reinforcement learning with function approximation benefit from general coverage conditions? 22063-22091
		Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem:
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. 22092-22112
		Chong Liu, Yu-Xiang Wang:
Global Optimization with Parametric Function Approximation. 22113-22136
		Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. 22137-22176
		Hanwen Liu, Zhenyu Weng, Yuesheng Zhu, Yadong Mu:
Trapdoor Normalization with Irreversible Ownership Verification. 22177-22187
		Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma:
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. 22188-22214
		Tianyi Liu, Zihao Xu, Hao He, Guang-Yuan Hao, Guang-He Lee, Hao Wang:
Taxonomy-Structured Domain Adaptation. 22215-22232
		Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell:
Dropout Reduces Underfitting. 22233-22248
		Biao Liu, Ning Xu, Jiaqi Lv, Xin Geng:
Revisiting Pseudo-Label for Single-Positive Multi-Label Learning. 22249-22265
		Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu:
Retrosynthetic Planning with Dual Value Networks. 22266-22276
		Xin Liu, Zixian Yang, Lei Ying:
Online Nonstochastic Control with Adversarial and Static Constraints. 22277-22288
		Tianci Liu, Tong Yang, Quan Zhang, Qi Lei:
Optimization for Amortized Inverse Problems. 22289-22319
		Xuefeng Liu, Takuma Yoneda, Chaoqi Wang, Matthew R. Walter, Yuxin Chen:
Active Policy Improvement from Multiple Black-box Oracles. 22320-22337
		Weitang Liu, Yi-Zhuang You, Ying-Wai Li, Jingbo Shang:
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space. 22338-22351
		Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao:
VectorMapNet: End-to-end Vectorized HD Map Learning. 22352-22369
		Xiangyu Liu, Kaiqing Zhang:
Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing. 22370-22419
		Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. 22420-22453
		Xuanzhou Liu, Lin Zhang, Jiaqi Sun, Yujiu Yang, Haiqin Yang:
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching. 22454-22472
		Zhuoran Liu, Zhengyu Zhao, Martha A. Larson:
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression. 22473-22487
		Mingzhou Liu, Xiangyu Zheng, Xinwei Sun, Fang Fang, Yizhou Wang:
Which Invariance Should We Transfer? A Causal Minimax Learning Approach. 22488-22527
		Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, Kilian Q. Weinberger:
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting. 22528-22538
		Zichuan Liu, Yuanyang Zhu, Chunlin Chen:
NA2Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning. 22539-22558
		Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen:
Contextual Combinatorial Bandits with Probabilistically Triggered Arms. 22559-22593
		Sam Lobel, Akhil Bagaria, George Konidaris:
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning. 22594-22613
		Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. 22614-22630
		Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts:
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. 22631-22648
		Noel Loo, Ramin M. Hasani, Mathias Lechner, Daniela Rus:
Dataset Distillation with Convexified Implicit Gradients. 22649-22674
		Aaron Lou, Stefano Ermon:
Reflected Diffusion Models. 22675-22701
		David R. Lovell, Dimity Miller, Jaiden Capra, Andrew P. Bradley:
Never mind the metrics - what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective. 22702-22757
		Songtao Lu:
Bilevel Optimization with Coupled Decision-Dependent Distributions. 22758-22789
		Yulong Lu:
Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective. 22790-22811
		Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh:
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. 22812-22824
		Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu:
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. 22825-22855
		Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. 22856-22879
		Xudong Lu, Kaisen Pan, Ge Yan, Jiaming Shan, Wenjie Wu, Junchi Yan:
QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark. 22880-22898
		Xuanchen Lu, Xiaolong Wang, Judith E. Fan:
Learning Dense Correspondences between Photos and Sketches. 22899-22916
		Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster:
Adversarial Cheap Talk. 22917-22941
		Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. 22942-22964
		Ekdeep Singh Lubana, Eric J. Bigelow, Robert P. Dick, David Scott Krueger, Hidenori Tanaka:
Mechanistic Mode Connectivity. 22965-23004
		Daniel Lundström, Meisam Razaviyayn:
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions. 23005-23032
		Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He, Tianrui Li:
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation. 23033-23044
		Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Image Restoration with Mean-Reverting Stochastic Differential Equations. 23045-23066
		Xinyu Luo, Christopher Musco, Cas Widdershoven:
Dimensionality Reduction for General KDE Mode Finding. 23067-23082
		Yuetian Luo, Zhimei Ren, Rina Barber:
Iterative Approximate Cross-Validation. 23083-23102
		Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, Jingkuan Song:
A Closer Look at Few-shot Classification Again. 23103-23123
		Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin, Wei Ju, Ming Zhang, Yizhou Sun:
HOPE: High-order Graph ODE For Modeling Interacting Dynamics. 23124-23139
		Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu:
Stabilizing GANs' Training with Brownian Motion Controller. 23140-23156
		Shahar Lutati, Lior Wolf:
OCD: Learning to Overfit with Conditional Diffusion Models. 23157-23169
		Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab:
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. 23170-23189
		Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding Plasticity in Neural Networks. 23190-23211
		Lixing Lyu, Wang Chi Cheung:
Bandits with Knapsacks: Advice on Time-Varying Demands. 23212-23238
		Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo:
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions. 23239-23263
		Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova:
Which Tricks are Important for Learning to Rank? 23264-23278
		Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik:
Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics. 23279-23300
		Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman:
LIV: Language-Image Representations and Rewards for Robotic Control. 23301-23320
		Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip H. S. Torr, Ser-Nam Lim:
Graph Inductive Biases in Transformers without Message Passing. 23321-23337
		Baorui Ma, Yu-Shen Liu, Zhizhong Han:
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping. 23338-23357
		Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster:
Learning Intuitive Policies Using Action Features. 23358-23372
		Ziye Ma, Igor Molybog, Javad Lavaei, Somayeh Sojoudi:
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points. 23373-23387
		Mingchen Ma, Christos Tzamos:
Buying Information for Stochastic Optimization. 23388-23411
		Yihan Ma, Zhikun Zhang, Ning Yu, Xinlei He, Michael Backes, Yun Shen, Yang Zhang:
Generated Graph Detection. 23412-23428
		Huan Ma, Qingyang Zhang, Changqing Zhang, Bingzhe Wu, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu:
Calibrating Multimodal Learning. 23429-23450
		Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus:
AutoCoreset: An Automatic Practical Coreset Construction Framework. 23451-23466
		Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin:
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability. 23467-23483
		Jessica Maghakian, Russell Lee, Mohammad Hajiesmaili, Jian Li, Ramesh K. Sitaraman, Zhenhua Liu:
Applied Online Algorithms with Heterogeneous Predictors. 23484-23497
		Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon:
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. 23498-23515
		Peihua Mai, Yan Pang:
Vertical Federated Graph Neural Network for Recommender System. 23516-23535
		Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? 23536-23557
		Anirudha Majumdar:
Fundamental Tradeoffs in Learning with Prior Information. 23558-23573
		Alan Malek, Virginia Aglietti, Silvia Chiappa:
Additive Causal Bandits with Unknown Graph. 23574-23589
		Dhruv Malik, Conor Igoe, Yuanzhi Li, Aarti Singh:
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality. 23590-23609
		Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora:
A Kernel-Based View of Language Model Fine-Tuning. 23610-23641
		Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic:
Performative Reinforcement Learning. 23642-23680
		Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi:
Differential Privacy has Bounded Impact on Fairness in Classification. 23681-23705
		Yishay Mansour, Richard Nock, Robert C. Williamson:
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. 23706-23742
		Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Pairwise Misranking Loss Surrogates. 23743-23802
		Anqi Mao, Mehryar Mohri, Yutao Zhong:
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. 23803-23828
		Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji:
Supported Trust Region Optimization for Offline Reinforcement Learning. 23829-23851
		Chengzhi Mao, Lingyu Zhang, Abhishek Vaibhav Joshi, Junfeng Yang, Hao Wang, Carl Vondrick:
Robust Perception through Equivariance. 23852-23870
		Anna C. Marbut, Katy McKinney-Bock, Travis J. Wheeler:
Reliable Measures of Spread in High Dimensional Latent Spaces. 23871-23885
		Tanguy Marchand, Regis Loeb, Ulysse Marteau-Ferey, Jean Ogier du Terrail, Arthur Pignet:
SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. 23886-23914
		Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso:
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. 23915-23936
		Adria Marcos-Morales, Matan Leibovich, Sreyas Mohan, Joshua Lawrence Vincent, Piyush Haluai, Mai Tan, Peter A. Crozier, Carlos Fernandez-Granda:
Evaluating Unsupervised Denoising Requires Unsupervised Metrics. 23937-23957
		Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados:
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. 23958-24004
		Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. 24005-24019
		Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh:
Quantized Distributed Training of Large Models with Convergence Guarantees. 24020-24044
		Juan Maroñas, Daniel Hernández-Lobato:
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. 24045-24081
		Samuele Marro, Michele Lombardi:
Computational Asymmetries in Robust Classification. 24082-24138
		Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski:
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. 24139-24172
		Satvik Mehul Mashkaria, Siddarth Krishnamoorthy, Aditya Grover:
Generative Pretraining for Black-Box Optimization. 24173-24197
		Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe:
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation. 24198-24213
		Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk:
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. 24214-24235
		Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan:
Communication-Constrained Bandits under Additive Gaussian Noise. 24236-24250
		Alessio Mazzetto, Eli Upfal:
Nonparametric Density Estimation under Distribution Drift. 24251-24270
		Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain:
PAC-Bayesian Generalization Bounds for Adversarial Generative Models. 24271-24290
		Brandon McKinzie, Vaishaal Shankar, Joseph Yitan Cheng, Yinfei Yang, Jonathon Shlens, Alexander T. Toshev:
Robustness in Multimodal Learning under Train-Test Modality Mismatch. 24291-24303
		Mohammad Mehrabi, Ryan A. Rossi:
A Model-free Closeness-of-influence Test for Features in Supervised Learning. 24304-24324
		Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. 24325-24360
		Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel:
Normalizing Flows for Interventional Density Estimation. 24361-24397
		Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das:
Reprogramming Pretrained Language Models for Antibody Sequence Infilling. 24398-24419
		Omid Memarrast, Linh Vu, Brian D. Ziebart:
Superhuman Fairness. 24420-24435
		Si Yi Meng, Robert M. Gower:
A Model-Based Method for Minimizing CVaR and Beyond. 24436-24456
		Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek F. Abdelzaher, Jiawei Han:
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning. 24457-24477
		Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina, Vianney Perchet:
On Preemption and Learning in Stochastic Scheduling. 24478-24516
		Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos:
Quantile Credit Assignment. 24517-24531
		Dmitry Metelev, Alexander Rogozin, Dmitry Kovalev, Alexander V. Gasnikov:
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? 24532-24554
		Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli:
Towards Theoretical Understanding of Inverse Reinforcement Learning. 24555-24591
		Nico Meyer, Daniel D. Scherer, Axel Plinge, Christopher Mutschler, Michael J. Hartmann:
Quantum Policy Gradient Algorithm with Optimized Action Decoding. 24592-24613
		Lucas Thibaut Meyer, Marc Schouler, Robert Alexander Caulk, Alejandro Ribés, Bruno Raffin:
Training Deep Surrogate Models with Large Scale Online Learning. 24614-24630
		David Henry Mguni, Haojun Chen, Taher Jafferjee, Jianhong Wang, Longfei Yue, Xidong Feng, Stephen Marcus McAleer, Feifei Tong, Jun Wang, Yaodong Yang:
MANSA: Learning Fast and Slow in Multi-Agent Systems. 24631-24658
		Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin:
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. 24659-24700
		Elissa Mhanna, Mohamad Assaad:
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function. 24701-24719
		Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Augmentations by Capturing Local Invariances. 24720-24736
		Gaspard Michel, Giannis Nikolentzos, Johannes F. Lutzeyer, Michalis Vazirgiannis:
Path Neural Networks: Expressive and Accurate Graph Neural Networks. 24737-24755
		Thomas Miconi:
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning. 24756-24774
		Eleni Miliotou, Panagiotis Kyriakis, Jason D. Hinman, Andrei Irimia, Paul Bogdan:
Generative Decoding of Visual Stimuli. 24775-24784
		Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. 24785-24811
		Ming Min, Ruimeng Hu, Tomoyuki Ichiba:
Directed Chain Generative Adversarial Networks. 24812-24830
		Seungki Min, Daniel Russo:
An Information-Theoretic Analysis of Nonstationary Bandit Learning. 24831-24849
		Hancheng Min, René Vidal, Enrique Mallada:
On the Convergence of Gradient Flow on Multi-layer Linear Models. 24850-24887
		Aaron Mishkin, Mert Pilanci:
Optimal Sets and Solution Paths of ReLU Networks. 24888-24924
		Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang:
The Numerical Stability of Hyperbolic Representation Learning. 24925-24949
		Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, Chelsea Finn:
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. 24950-24962
		Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou:
Diffusion Based Representation Learning. 24963-24982
		Yujie Mo, Yajie Lei, Jialie Shen, Xiaoshuang Shi, Heng Tao Shen, Xiaofeng Zhu:
Disentangled Multiplex Graph Representation Learning. 24983-25005
		Shentong Mo, Pedro Morgado:
A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition. 25006-25017
		Zhanfeng Mo, Haosen Shi, Sinno Jialin Pan:
Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint. 25018-25036
		Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi:
Text-To-Concept (and Back) via Cross-Model Alignment. 25037-25060
		Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland:
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. 25061-25081
		Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Special Properties of Gradient Descent with Large Learning Rates. 25082-25104
		Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra:
Neural Inverse Operators for Solving PDE Inverse Problems. 25105-25139
		Paul Monchot, Loic Coquelin, Sébastien Julien Petit, Sébastien Marmin, Erwan Le Pennec, Nicolas Fischer:
Input uncertainty propagation through trained neural networks. 25140-25173
		Andrea Montanari, Eric Weiner:
Compressing Tabular Data via Latent Variable Estimation. 25174-25208
		Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi:
An SDE for Modeling SAM: Theory and Insights. 25209-25253
		Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa:
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic. 25254-25274
		Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe:
WL meet VC. 25275-25302
		Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy:
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. 25303-25336
		Antoine Moulin, Gergely Neu:
Optimistic Planning by Regularized Dynamic Programming. 25337-25357
		Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi:
Neural signature kernels as infinite-width-depth-limits of controlled ResNets. 25358-25425
		Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Hervé Jégou, Jakob Verbeek:
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. 25426-25443
		Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs4BO: In-Context Learning for Bayesian Optimization. 25444-25470
		Johannes Müller, Marius Zeinhofer:
Achieving High Accuracy with PINNs via Energy Natural Gradient Descent. 25471-25485
		Andreas Munk, Alexander Mead, Frank Wood:
Uncertain Evidence in Probabilistic Models and Stochastic Simulators. 25486-25500
		Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration. 25501-25522
		Tomoya Murata, Taiji Suzuki:
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. 25523-25548
		Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler:
Efficiently predicting high resolution mass spectra with graph neural networks. 25549-25562
		Marco Mussi, Alberto Maria Metelli, Marcello Restelli:
Dynamical Linear Bandits. 25563-25587
		Ofir Nabati, Guy Tennenholtz, Shie Mannor:
Representation-Driven Reinforcement Learning. 25588-25603
		Adel Nabli, Edouard Oyallon:
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. 25604-25626
		Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain:
Multi-User Reinforcement Learning with Low Rank Rewards. 25627-25659
		Thomas Nagler:
Statistical Foundations of Prior-Data Fitted Networks. 25660-25676
		Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong:
Do Machine Learning Models Learn Statistical Rules Inferred from Data? 25677-25693
		Ilan Naiman, Nimrod Berman, Omri Azencot:
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation. 25694-25717
		Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr:
Effectively Using Public Data in Privacy Preserving Machine Learning. 25718-25732
		Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah:
Counterfactual Identifiability of Bijective Causal Models. 25733-25754
		Somjit Nath, Gopeshh Raaj Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou:
Discovering Object-Centric Generalized Value Functions From Pixels. 25755-25768
		Michal Nauman, Marek Cygan:
On Many-Actions Policy Gradient. 25769-25789
		Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. 25790-25816
		Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan, Hamsa Balakrishnan:
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation. 25817-25833
		Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht:
Geometric Autoencoders - What You See is What You Decode. 25834-25857
		Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani:
Action Matching: Learning Stochastic Dynamics from Samples. 25858-25889
		Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M. Halappanavar:
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains. 25890-25903
		Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover:
ClimaX: A foundation model for weather and climate. 25904-25938
		Hoai-An Nguyen, Ching-An Cheng:
Provable Reset-free Reinforcement Learning by No-Regret Reduction. 25939-25955
		Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, Tan Minh Nguyen:
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature. 25956-25979
		Tri Nguyen, Shahana Ibrahim, Xiao Fu:
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach. 25980-26007
		Khai Nguyen, Dang Nguyen, Nhat Ho:
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. 26008-26030
		Xuan Son Nguyen, Shuo Yang:
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach. 26031-26062
		Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Simple Disentanglement of Style and Content in Visual Representations. 26063-26086
		Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, Zhixuan Liang:
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL. 26087-26105
		Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida I. Wang, Xi Victoria Lin:
LEVER: Learning to Verify Language-to-Code Generation with Execution. 26106-26128
		Zixuan Ni, Longhui Wei, Siliang Tang, Yueting Zhuang, Qi Tian:
Continual Vision-Language Representation Learning with Off-Diagonal Information. 26129-26149
		Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren:
Attributing Image Generative Models using Latent Fingerprints. 26150-26165
		Guanyu Nie, Yididiya Y. Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn:
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback. 26166-26198
		Yaniv Nikankin, Niv Haim, Michal Irani:
SinFusion: Training Diffusion Models on a Single Image or Video. 26199-26214
		Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh:
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge. 26215-26227
		Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov:
Anti-Exploration by Random Network Distillation. 26228-26244
		Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara:
Input Perturbation Reduces Exposure Bias in Diffusion Models. 26245-26265
		Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki:
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. 26266-26282
		Georgy Noarov, Aaron Roth:
The Statistical Scope of Multicalibration. 26283-26310
		Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox:
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling. 26311-26325
		Azade Nova, Hanjun Dai, Dale Schuurmans:
Gradient-Free Structured Pruning with Unlabeled Data. 26326-26341
		Zachary Novack, Julian J. McAuley, Zachary Chase Lipton, Saurabh Garg:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. 26342-26362
		Georgii Sergeevich Novikov, Daniel Bershatsky, Julia Gusak, Alex Shonenkov, Denis Valerievich Dimitrov, Ivan V. Oseledets:
Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction. 26363-26381
		Brendan O'Donoghue:
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization. 26382-26402
		Junsoo Oh, Chulhee Yun:
Provable Benefit of Mixup for Finding Optimal Decision Boundaries. 26403-26450
		Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola:
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. 26451-26473
		Guy Ohayon, Theo Joseph Adrai, Michael Elad, Tomer Michaeli:
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality. 26474-26494
		Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez:
On the Within-Group Fairness of Screening Classifiers. 26495-26516
		Kazusato Oko, Shunta Akiyama, Taiji Suzuki:
Diffusion Models are Minimax Optimal Distribution Estimators. 26517-26582
		Raphaël Olivier, Bhiksha Raj:
How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy. 26583-26598
		Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit:
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. 26599-26618
		Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta:
Measuring the Impact of Programming Language Distribution. 26619-26645
		Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D'Amour, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou:
When does Privileged information Explain Away Label Noise? 26646-26669
		Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De:
Resurrecting Recurrent Neural Networks for Long Sequences. 26670-26698
		Yidong Ouyang, Liyan Xie, Guang Cheng:
Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process. 26699-26723
		Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis:
On the Role of Attention in Prompt-tuning. 26724-26768
		Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. 26769-26791
		Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh:
Extrapolative Controlled Sequence Generation via Iterative Refinement. 26792-26808
		Avik Pal, Alan Edelman, Christopher Vincent Rackauckas:
Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed! 26809-26819
		Sourav Pal, Zhanpeng Zeng, Sathya N. Ravi, Vikas Singh:
Controlled Differential Equations on Long Sequences via Non-standard Wavelets. 26820-26836
		Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, Dan Hendrycks:
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. 26837-26867
		Erlin Pan, Zhao Kang:
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering. 26868-26877
		Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio:
Better Training of GFlowNets with Local Credit and Incomplete Trajectories. 26878-26890
		Hongyi Pan, Xin Zhu, Salih Furkan Atici, Ahmet Enis Çetin:
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer. 26891-26903
		Ioannis Panageas, Stratis Skoulakis, Luca Viano, Xiao Wang, Volkan Cevher:
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees. 26904-26930
		Kunjal Panchal, Sunav Choudhary, Subrata Mitra, Koyel Mukherjee, Somdeb Sarkhel, Saayan Mitra, Hui Guan:
Flash: Concept Drift Adaptation in Federated Learning. 26931-26962
		Deep Shankar Pandey, Qi Yu:
Learn to Accumulate Evidence from All Training Samples: Theory and Practice. 26963-26989
		Qi Pang, Lun Wang, Shuai Wang, Wenting Zheng, Dawn Song:
Secure Federated Correlation Test and Entropy Estimation. 26990-27010
		Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora:
Task-Specific Skill Localization in Fine-tuned Language Models. 27011-27033
		Junyoung Park, Jeongyoun Ahn, Cheolwoo Park:
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation. 27034-27047
		Jin-Hwi Park, Jaesung Choe, Inhwan Bae, Hae-Gon Jeon:
Learning Affinity with Hyperbolic Representation for Spatial Propagation. 27048-27073
		Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry:
TRAK: Attributing Model Behavior at Scale. 27074-27113
		Jungwuk Park, Dong-Jun Han, Soyeong Kim, Jaekyun Moon:
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization. 27114-27131
		Sejun Park, Kihun Hong, Ganguk Hwang:
Towards Understanding Ensemble Distillation in Federated Learning. 27132-27187
		Seobin Park, Dongjin Kim, Sungyong Baik, Tae Hyun Kim:
Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows. 27188-27203
		Jinseong Park, Hoki Kim, Yujin Choi, Jaewook Lee:
Differentially Private Sharpness-Aware Training. 27204-27224
		Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel:
Controllability-Aware Unsupervised Skill Discovery. 27225-27245
		Seohong Park, Sergey Levine:
Predictable MDP Abstraction for Unsupervised Model-Based RL. 27246-27268
		Sungwoo Park, Byoungwoo Park, Moontae Lee, Changhee Lee:
Neural Stochastic Differential Games for Time-series Analysis. 27269-27293
		Jisun Park, Ernest K. Ryu:
Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations. 27294-27345
		Paavo Parmas, Takuma Seno, Yuma Aoki:
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators. 27346-27377
		Advait U. Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai:
PAC Generalization via Invariant Representations. 27378-27400
		Farhad Pashakhanloo, Alexei Koulakov:
Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network. 27401-27419
		Saro Passaro, C. Lawrence Zitnick:
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs. 27420-27438
		Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro:
Federated Online and Bandit Convex Optimization. 27439-27460
		Edward Pearce-Crump:
Brauer's Group Equivariant Neural Networks. 27461-27482
		Edward Pearce-Crump:
How Jellyfish Characterise Alternating Group Equivariant Neural Networks. 27483-27495
		Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin:
Can Large Language Models Reason about Program Invariants? 27496-27520
		Zhengqi Pei, Shuhui Wang:
Dynamics-inspired Neuromorphic Visual Representation Learning. 27521-27541
		Peifeng Gao, Qianqian Xu, Peisong Wen, Zhiyong Yang, Huiyang Shao, Qingming Huang:
Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation. 27542-27563
		Jaakko Peltonen, Wen Xu, Timo Nummenmaa, Jyrki Nummenmaa:
Fair Neighbor Embedding. 27564-27584
		Liangzu Peng, Paris Giampouras, René Vidal:
The Ideal Continual Learner: An Agent That Never Forgets. 27585-27610
		Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma:
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. 27611-27629
		Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal:
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. 27630-27641
		Binyamin Perets, Mark Kozdoba, Shie Mannor:
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown. 27642-27667
		Lorenzo Perini, Paul-Christian Bürkner, Arto Klami:
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. 27668-27679
		Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan:
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation. 27680-27708
		Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi:
Certifying Ensembles: A General Certification Theory with S-Lipschitzness. 27709-27736
		Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. 27737-27821
		Chi Bach Pham, Wynita Griggs, James Saunderson:
A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP. 27822-27839
		Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien:
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability. 27840-27853
		Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen:
HyperTuning: Toward Adapting Large Language Models without Back-propagation. 27854-27875
		Hannah Pinson, Joeri Lenaerts, Vincent Ginis:
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies. 27876-27906
		Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov:
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. 27907-27947
		Lena Podina, Brydon Eastman, Mohammad Kohandel:
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data. 27948-27956
		Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas:
Sequential Kernelized Independence Testing. 27957-27993
		Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli:
Truncating Trajectories in Monte Carlo Reinforcement Learning. 27994-28042
		Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré:
Hyena Hierarchy: Towards Larger Convolutional Language Models. 28043-28078
		Samuele Pollaci:
Spurious Valleys and Clustering Behavior of Neural Networks. 28079-28099
		Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen:
Multisample Flow Matching: Straightening Flows with Minibatch Couplings. 28100-28127
		Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed:
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case. 28128-28150
		Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki:
Test-time Adaptation with Slot-Centric Models. 28151-28166
		Drew Prinster, Suchi Saria, Anqi Liu:
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift. 28167-28190
		Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman:
Equivariant Polynomials for Graph Neural Networks. 28191-28222
		Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi:
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining. 28223-28243
		Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner:
An Effective Meaningful Way to Evaluate Survival Models. 28244-28276
		Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, Yanyan Lan:
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D. 28277-28299
		Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low:
Collaborative Causal Inference with Fair Incentives. 28300-28320
		Congyu Qiao, Ning Xu, Jiaqi Lv, Yi Ren, Xin Geng:
FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning. 28321-28336
		Guanghui Qin, Benjamin Van Durme:
Nugget: Neural Agglomerative Embeddings of Text. 28337-28350
		Haotong Qin, Mingyuan Zhang, Yifu Ding, Aoyu Li, Zhongang Cai, Ziwei Liu, Fisher Yu, Xianglong Liu:
BiBench: Benchmarking and Analyzing Network Binarization. 28351-28388
		Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang:
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. 28389-28421
		Xin Qiu, Risto Miikkulainen:
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search. 28422-28447
		Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson:
Simple and Fast Group Robustness by Automatic Feature Reweighting. 28448-28467
		Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer:
DRCFS: Doubly Robust Causal Feature Selection. 28468-28491
		Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever:
Robust Speech Recognition via Large-Scale Weak Supervision. 28492-28518
		Matthew Raffel, Drew Penney, Lizhong Chen:
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation. 28519-28530
		Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz:
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. 28531-28548
		Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt P. Dubhashi, Hamid Krim:
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework. 28549-28577
		Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. 28578-28597
		Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste:
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. 28598-28617
		Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman:
SpotEM: Efficient Video Search for Episodic Memory. 28618-28636
		Sameera Ramasinghe, Lachlan Ewen MacDonald, Moshiur R. Farazi, Hemanth Saratchandran, Simon Lucey:
How much does Initialization Affect Generalization? 28637-28655
		Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz:
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization. 28656-28679
		Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari:
A Picture of the Space of Typical Learnable Tasks. 28680-28700
		Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang, Yang Yu:
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning. 28701-28717
		Ran Ran, Xinwei Luo, Wei Wang, Tao Liu, Gang Quan, Xiaolin Xu, Caiwen Ding, Wujie Wen:
SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference. 28718-28728
		Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A. Poggio:
Feature learning in deep classifiers through Intermediate Neural Collapse. 28729-28745
		Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. 28746-28767
		Jishnu Ray Chowdhury, Cornelia Caragea:
Beam Tree Recursive Cells. 28768-28791
		Jishnu Ray Chowdhury, Cornelia Caragea:
Monotonic Location Attention for Length Generalization. 28792-28808
		Ofir Razon, Yoav Harris, Shahar Gottlieb, Dan Carmon, Ofir David, Ido Kaminer:
Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences. 28809-28842
		Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt:
Neural networks trained with SGD learn distributions of increasing complexity. 28843-28863
		Isaac Reid, Krzysztof Marcin Choromanski, Valerii Likhosherstov, Adrian Weller:
Simplex Random Features. 28864-28888
		Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang:
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. 28889-28913
		Zhaolin Ren, Yujie Tang, Na Li:
Escaping saddle points in zeroth-order optimization: the power of two-point estimators. 28914-28975
		Jiaxiang Ren, Yang Zhou, Jiayin Jin, Lingjuan Lyu, Da Yan:
Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing. 28976-29008
		Alex Daniel Reneau, Jerry Yao-Chieh Hu, Ammar Gilani, Han Liu:
Feature Programming for Multivariate Time Series Prediction. 29009-29029
		Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, Soheil Feizi:
Run-off Election: Improved Provable Defense against Data Poisoning Attacks. 29030-29050
		Spencer M. Richards, Jean-Jacques E. Slotine, Navid Azizan, Marco Pavone:
Learning Control-Oriented Dynamical Structure from Data. 29051-29062
		Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill:
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. 29063-29081
		Alexandre Rio, Merwan Barlier, Igor Colin, Marta Soare:
Multi-Agent Best Arm Identification with Private Communications. 29082-29102
		Nicholas Rittler, Kamalika Chaudhuri:
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. 29103-29129
		Yeonju Ro, Zhangyang Wang, Vijay Chidambaram, Aditya Akella:
Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit. 29130-29142
		Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella:
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. 29143-29160
		Rafael Rodríguez-Sánchez, Benjamin Adin Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris:
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. 29161-29178
		Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh:
Improving Fair Training under Correlation Shifts. 29179-29209
		Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney:
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. 29210-29231
		Haolin Ruan, Siyu Zhou, Zhi Chen, Chin Pang Ho:
Robust Satisficing MDPs. 29232-29258
		Mark Rucker, Yinglun Zhu, Paul Mineiro:
Infinite Action Contextual Bandits with Reusable Data Exhaust. 29259-29274
		Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. 29275-29290
		David Rügamer:
A New PHO-rmula for Improved Performance of Semi-Structured Networks. 29291-29305
		David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter:
Geometric Clifford Algebra Networks. 29306-29337
		Davor Runje, Sharath M. Shankaranarayana:
Constrained Monotonic Neural Networks. 29338-29353
		Phillip Rust, Anders Søgaard:
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models. 29354-29387
		Raif M. Rustamov, Subhabrata Majumdar:
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs. 29388-29415
		Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov:
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. 29416-29440
		Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer:
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles. 29441-29454
		Yves Rychener, Daniel Kuhn, Tobias Sutter:
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective. 29455-29472
		Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka:
Sequential Monte Carlo Learning for Time Series Structure Discovery. 29473-29489
		El Mehdi Saad, Nicolas Verzelen, Alexandra Carpentier:
Active Ranking of Experts Based on their Performances in Many Tasks. 29490-29513
		Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl S. Motahari, Babak H. Khalaj:
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes. 29514-29541
		Vin Sachidananda, Ziyi Yang, Chenguang Zhu:
Global Selection of Contrastive Batches via Optimization on Sample Permutations. 29542-29562
		Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik:
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. 29563-29648
		Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram:
End-to-end Differentiable Clustering with Associative Memories. 29649-29670
		Eden Saig, Nir Rosenfeld:
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement. 29671-29696
		Shota Saito, Mark Herbster:
Multi-class Graph Clustering via Approximated Effective p-Resistance. 29697-29733
		Yuta Saito, Qingyang Ren, Thorsten Joachims:
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. 29734-29759
		Shinsaku Sakaue, Taihei Oki:
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L♮-Convex Function Minimization. 29760-29776
		Otmane Sakhi, Pierre Alquier, Nicolas Chopin:
PAC-Bayesian Offline Contextual Bandits With Guarantees. 29777-29799
		Sudeep Salgia:
Provably and Practically Efficient Neural Contextual Bandits. 29800-29844
		Sudeep Salgia, Qing Zhao:
Distributed Linear Bandits under Communication Constraints. 29845-29875
		David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. 29876-29893
		Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry:
Raising the Cost of Malicious AI-Powered Image Editing. 29894-29918
		Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. 29919-29936
		Tom Sander, Pierre Stock, Alexandre Sablayrolles:
TAN Without a Burn: Scaling Laws of DP-SGD. 29937-29949
		Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh K. Iyer, Abir De:
Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models. 29950-29970
		Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto:
Whose Opinions Do Language Models Reflect? 29971-30004
		Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. 30005-30021
		Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Random Teachers are Good Teachers. 30022-30041
		Remo Sasso, Michelangelo Conserva, Paulo E. Rauber:
Posterior Sampling for Deep Reinforcement Learning. 30042-30061
		Ryoma Sato:
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure. 30062-30079
		Naoki Sato, Hideaki Iiduka:
Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. 30080-30104
		Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila:
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis. 30105-30118
		Andrey V. Savchenko:
Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction. 30119-30129
		Naman Saxena, Subhojyoti Khastagir, Shishir Kolathaya, Shalabh Bhatnagar:
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search. 30130-30203
		Philip Schär, Michael Habeck, Daniel Rudolf:
Gibbsian Polar Slice Sampling. 30204-30223
		Andreas Schlaginhaufen, Maryam Kamgarpour:
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning. 30224-30251
		Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel:
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks. 30252-30284
		Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro:
Deterministic equivalent and error universality of deep random features learning. 30285-30320
		Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz:
The Acquisition of Physical Knowledge in Generative Neural Networks. 30321-30341
		Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin:
Modality-Agnostic Variational Compression of Implicit Neural Representations. 30342-30364
		Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. 30365-30380
		Antonio Sclocchi, Mario Geiger, Matthieu Wyart:
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. 30381-30405
		Michael Sedlmayer, Dang-Khoa Nguyen, Radu Ioan Bot:
A Fast Optimistic Method for Monotone Variational Inequalities. 30406-30438
		José Ignacio Segovia-Martín, Santiago Mazuelas, Anqi Liu:
Double-Weighting for Covariate Shift Adaptation. 30439-30457
		Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer:
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. 30458-30490
		Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris:
Variational Autoencoding Neural Operators. 30491-30522
		Patrick Seifner, Ramsés J. Sánchez:
Neural Markov Jump Processes. 30523-30552
		Jeremy Sellier, Petros Dellaportas:
Bayesian online change point detection with Hilbert space approximate Student-t process. 30553-30569
		Mark Sellke:
Incentivizing Exploration with Linear Contexts and Combinatorial Actions. 30570-30583
		Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei:
Explainability as statistical inference. 30584-30612
		Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel:
Multi-View Masked World Models for Visual Robotic Manipulation. 30613-30632
		Daniel Severo, James Townsend, Ashish J. Khisti, Alireza Makhzani:
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding. 30633-30645
		Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry:
ModelDiff: A Framework for Comparing Learning Algorithms. 30646-30688
		Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Auxiliary Learning as an Asymmetric Bargaining Game. 30689-30705
		Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen:
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models. 30706-30775
		Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He, Yuhang Jiang, Xiangyang Ji:
Complementary Attention for Multi-Agent Reinforcement Learning. 30776-30793
		Ron Shapira Weber, Oren Freifeld:
Regularization-free Diffeomorphic Temporal Alignment Nets. 30794-30826
		Arsalan Sharifnassab, Richard S. Sutton:
Toward Efficient Gradient-Based Value Estimation. 30827-30849
		Louis Sharrock, Christopher Nemeth:
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. 30850-30882
		Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman:
On Kinetic Optimal Probability Paths for Generative Models. 30883-30907
		Shubhanshu Shekhar, Aaditya Ramdas:
Sequential Changepoint Detection via Backward Confidence Sequences. 30908-30930
		Alexander Shekhovtsov:
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator. 30931-30955
		Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani:
Towards Understanding and Improving GFlowNet Training. 30956-30975
		Maohao Shen, Yuheng Bu, Gregory W. Wornell:
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation. 30976-30991
		Han Shen, Tianyi Chen:
On Penalty-based Bilevel Gradient Descent Method. 30992-31015
		Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. 31016-31029
		Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
Cross-Modal Fine-Tuning: Align then Refine. 31030-31056
		Yu Shen, Xijun Wang, Peng Gao, Ming C. Lin:
Auxiliary Modality Learning with Generalized Curriculum Distillation. 31057-31076
		Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal:
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. 31077-31093
		Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU. 31094-31116
		Uri Sherman, Tomer Koren, Yishay Mansour:
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. 31117-31150
		Aleksandr Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli:
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. 31151-31209
		Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou:
Large Language Models Can Be Easily Distracted by Irrelevant Context. 31210-31227
		Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, Jishen Zhao:
Everyone's Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval. 31228-31242
		Ming Shi, Yingbin Liang, Ness B. Shroff:
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. 31243-31268
		Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, Dacheng Tao:
Improving the Model Consistency of Decentralized Federated Learning. 31269-31291
		Dachuan Shi, Chaofan Tao, Ying Jin, Zhendong Yang, Chun Yuan, Jiaqi Wang:
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers. 31292-31311
		Jiaxin Shi, Ke Alexander Wang, Emily B. Fox:
Sequence Modeling with Multiresolution Convolutional Memory. 31312-31327
		Lei Shi, Jingshen Wang, Tianhao Wu:
Statistical Inference on Multi-armed Bandits with Delayed Feedback. 31328-31352
		Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. 31353-31388
		Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu:
On the Complexity of Bayesian Generalization. 31389-31407
		Liangliang Shi, Gu Zhang, Haoyu Zhen, Jintao Fan, Junchi Yan:
Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective. 31408-31421
		Andy Shih, Dorsa Sadigh, Stefano Ermon:
Long Horizon Temperature Scaling. 31422-31434
		Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space. 31435-31488
		Dongseok Shim, Seungjae Lee, H. Jin Kim:
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. 31489-31503
		Sungbin Shin, Yohan Jo, Sungsoo Ahn, Namhoon Lee:
A Closer Look at the Intervention Procedure of Concept Bottleneck Models. 31504-31520
		WooSeok Shin, Byung Hoon Lee, Jin Sob Kim, Hyun Joon Park, Sung Won Han:
MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement. 31521-31538
		Yongho Shin, Changyeol Lee, Gukryeol Lee, Hyung-Chan An:
Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis. 31539-31561
		Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim, Honguk Woo:
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill. 31562-31578
		Yooju Shin, Susik Yoon, Hwanjun Song, Dongmin Park, Byunghyun Kim, Jae-Gil Lee, Byung Suk Lee:
Context Consistency Regularization for Label Sparsity in Time Series. 31579-31595
		Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley:
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting. 31596-31612
		Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop:
Exphormer: Sparse Transformers for Graphs. 31613-31632
		Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Matan Fintz, Gérard G. Medioni:
Synthetic data for model selection. 31633-31656
		Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P. Bennett:
Probabilistic Attention-to-Influence Neural Models for Event Sequences. 31657-31674
		Madhumitha Shridharan, Garud Iyengar:
Causal Bounds in Quasi-Markovian Graphs. 31675-31692
		Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Repository-Level Prompt Generation for Large Language Models of Code. 31693-31715
		Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long:
CLIPood: Generalizing CLIP to Out-of-Distributions. 31716-31731
		Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov:
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. 31732-31753
		Ali Siahkoohi, Rudy Morel, Maarten V. de Hoop, Erwan Allys, Grégory Sainton, Taichi Kawamura:
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data. 31754-31772
		Julian Sieber, Johann Gehringer:
Quantitative Universal Approximation Bounds for Deep Belief Networks. 31773-31787
		David Simchi-Levi, Chonghuan Wang:
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk. 31788-31799
		Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. 31800-31851
		James B. Simon, Maksis Knutins, Ziyin Liu, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht:
On the Stepwise Nature of Self-Supervised Learning. 31852-31876
		Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo De Oliveira Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan:
Hindsight Learning for MDPs with Exogenous Inputs. 31877-31914
		Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, Yaniv Taigman:
Text-To-4D Dynamic Scene Generation. 31915-31929
		Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. 31930-31968
		Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell:
When do Minimax-fair Learning and Empirical Risk Minimization Coincide? 31969-31989
		Martin Sípka, Johannes C. B. Dietschreit, Lukás Grajciar, Rafael Gómez-Bombarelli:
Differentiable Simulations for Enhanced Sampling of Rare Events. 31990-32007
		Chawin Sitawarin, Florian Tramèr, Nicholas Carlini:
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. 32008-32032
		Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. 32033-32058
		Oliver Slumbers, David Henry Mguni, Stefano B. Blumberg, Stephen Marcus McAleer, Yaodong Yang, Jun Wang:
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems. 32059-32087
		Paloma Sodhi, Felix Wu, Ethan R. Elenberg, Kilian Q. Weinberger, Ryan McDonald:
On the Effectiveness of Offline RL for Dialogue Response Generation. 32088-32104
		Alexander Soen, Hisham Husain, Richard Nock:
Fair Densities via Boosting the Sufficient Statistics of Exponential Families. 32105-32144
		Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci:
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. 32145-32168
		Samuel Sokota, Ryan D'Orazio, Chun Kai Ling, David J. Wu, J. Zico Kolter, Noam Brown:
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games. 32169-32193
		Jiwoo Son, Minsu Kim, Hyeonah Kim, Jinkyoo Park:
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization. 32194-32210
		Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever:
Consistency Models. 32211-32252
		Xujie Song, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Chen Chen, Bo Cheng, Bo Zhang, Junqing Wei, Xiaoming Simon Wang:
LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control. 32253-32272
		Zifan Song, Xiao Gong, Guosheng Hu, Cairong Zhao:
Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations. 32273-32287
		Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling:
Latent Traversals in Generative Models as Potential Flows. 32288-32303
		Bingqing Song, Prashant Khanduri, Xinwei Zhang, Jinfeng Yi, Mingyi Hong:
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. 32304-32330
		Jaeyun Song, Sungyub Kim, Eunho Yang:
RGE: A Repulsive Graph Rectification for Node Classification via Influence. 32331-32348
		Zhenqiao Song, Lei Li:
Importance Weighted Expectation-Maximization for Protein Sequence Design. 32349-32364
		Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang:
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. 32365-32417
		Zhao Song, Xin Yang, Yuanyuan Yang, Lichen Zhang:
Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance. 32418-32462
		Zhao Song, Mingquan Ye, Junze Yin, Lichen Zhang:
A Nearly-Optimal Bound for Fast Regression with ℓ∞ Guarantee. 32463-32482
		Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, Arash Vahdat:
Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation. 32483-32498
		Paul Soulos, Edward J. Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao:
Differentiable Tree Operations Promote Compositional Generalization. 32499-32520
		Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei:
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. 32521-32539
		Chandler Squires, Anna Seigal, Salil S. Bhate, Caroline Uhler:
Linear Causal Disentanglement via Interventions. 32540-32560
		Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. 32561-32574
		Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau:
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. 32575-32597
		David Stein, Silvia Di Gregorio, Bjoern Andres:
Partial Optimality in Cubic Correlation Clustering. 32598-32617
		Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty:
MODeL: Memory Optimizations for Deep Learning. 32618-32632
		Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez:
Improving Expert Predictions with Conformal Prediction. 32633-32653
		Grant P. Strimel, Yi Xie, Brian John King, Martin Radfar, Ariya Rastrow, Athanasios Mouchtaris:
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers. 32654-32676
		Diego Stucchi, Paolo Rizzo, Nicolò Folloni, Giacomo Boracchi:
Kernel QuantTree. 32677-32697
		Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern H. Menze, Ulrich Bauer:
Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes. 32698-32727
		Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu:
Towards Robust Graph Incremental Learning on Evolving Graphs. 32728-32748
		Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella:
DUET: 2D Structured and Approximately Equivariant Representations. 32749-32769
		Min-Kook Suh, Seung-Woo Seo:
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels. 32770-32782
		Yang Sui, Yukun Huang, Hongtu Zhu, Fan Zhou:
Adversarial Learning of Distributional Reinforcement Learning. 32783-32796
		Theodore R. Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta:
Distilling Internet-Scale Vision-Language Models into Embodied Agents. 32797-32818
		Zhuo Sun, Alessandro Barp, François-Xavier Briol:
Vector-Valued Control Variates. 32819-32846
		Wenfang Sun, Yingjun Du, Xiantong Zhen, Fan Wang, Ling Wang, Cees G. M. Snoek:
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks. 32847-32858
		Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai:
Revisiting Sampling for Combinatorial Optimization. 32859-32874
		Zequn Sun, Jiacheng Huang, Xiaozhou Xu, Qijin Chen, Weijun Ren, Wei Hu:
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings. 32875-32885
		Chunlin Sun, Shang Liu, Xiaocheng Li:
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming. 32886-32912
		Hu Sun, Ward Manchester, Meng Jin, Yang Liu, Yang Chen:
Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis. 32913-32935
		Jennifer J. Sun, Markus Marks, Andrew Wesley Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, Sebastian Oleszko, Zachary Partridge, Milan Peelman, Alice Robie, Catherine E. Schretter, Keith Sheppard, Chao Sun, Param Uttarwar, Julian Morgan Wagner, Erik Werner, Joseph Parker, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy:
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior. 32936-32990
		Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao:
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. 32991-33013
		Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li:
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis. 33014-33043
		Wei Sun, Asterios Tsiourvas:
Learning Prescriptive ReLU Networks. 33044-33060
		Junshu Sun, Shuhui Wang, Xinzhe Han, Zhe Xue, Qingming Huang:
All in a Row: Compressed Convolution Networks for Graphs. 33061-33076
		Tao Sun, Qingsong Wang, Dongsheng Li, Bao Wang:
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. 33077-33099
		Jiachen Sun, Jiongxiao Wang, Weili Nie, Zhiding Yu, Zhuoqing Mao, Chaowei Xiao:
A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification. 33100-33114
		Shikun Sun, Longhui Wei, Junliang Xing, Jia Jia, Qi Tian:
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation. 33115-33134
		Zhiqing Sun, Yiming Yang, Shinjae Yoo:
A Neural PDE Solver with Temporal Stencil Modeling. 33135-33155
		Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang:
Feature Expansion for Graph Neural Networks. 33156-33176
		Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye, Yang Yu:
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning. 33177-33194
		Mukund Sundararajan, Walid Krichene:
Inflow, Outflow, and Reciprocity in Machine Learning. 33195-33208
		Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun:
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction. 33209-33228
		André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai:
Tuning Computer Vision Models With Task Rewards. 33229-33239
		Wesley A. Suttle, Amrit S. Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha:
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. 33240-33267
		Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi:
Tight and fast generalization error bound of graph embedding in metric space. 33268-33284
		Erik Sverdrup, Yifan Cui:
Proximal Causal Learning of Conditional Average Treatment Effects. 33285-33298
		Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu:
Inverse Reinforcement Learning without Reinforcement Learning. 33299-33318
		Kirk Swanson, Jake Lawrence Williams, Eric M. Jonas:
Von Mises Mixture Distributions for Molecular Conformation Generation. 33319-33342
		Yasa Syed, Guanyang Wang:
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations. 33343-33364
		Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai, Akshara Rai:
Adaptive Coordination in Social Embodied Rearrangement. 33365-33380
		Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West:
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods. 33381-33395
		Wai Ming Tai, Bryon Aragam:
Learning Mixtures of Gaussians with Censored Data. 33396-33415
		Shokichi Takakura, Taiji Suzuki:
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. 33416-33447
		Makoto Takamoto, Francesco Alesiani, Mathias Niepert:
Learning Neural PDE Solvers with Parameter-Guided Channel Attention. 33448-33467
		Kei Takemura:
Contextual Conservative Interleaving Bandits. 33468-33489
		Shion Takeno, Yu Inatsu, Masayuki Karasuyama:
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds. 33490-33515
		Shion Takeno, Masahiro Nomura, Masayuki Karasuyama:
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. 33516-33533
		Zeren Tan, Yang Tian:
Robust Explanation for Free or At the Cost of Faithfulness. 33534-33562
		Xiaoyu Tan, Lin Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Yinghui Xu, Peng Cui, Yuan Qi:
Provably Invariant Learning without Domain Information. 33563-33580
		Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine:
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. 33581-33598
		Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer, Yaodong Yang:
Regret-Minimizing Double Oracle for Extensive-Form Games. 33599-33615
		Hao Tang, Kevin Ellis:
From Perception to Programs: Regularize, Overparameterize, and Amortize. 33616-33631
		Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko:
Understanding Self-Predictive Learning for Reinforcement Learning. 33632-33656
		Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko:
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. 33657-33673
		Huayi Tang, Yong Liu:
Towards Understanding Generalization of Graph Neural Networks. 33674-33719
		Yunhao Tang, Rémi Munos:
Towards a better understanding of representation dynamics under TD-learning. 33720-33738
		Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko:
VA-learning as a more efficient alternative to Q-learning. 33739-33757
		Ling Tang, Wen Shen, Zhanpeng Zhou, Yuefeng Chen, Quanshi Zhang:
Defects of Convolutional Decoder Networks in Frequency Representation. 33758-33791
		Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Longfei Li, Jun Zhou:
Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding. 33792-33803
		Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo:
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization. 33804-33815
		Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, Mingyuan Zhou:
POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models. 33816-33832
		Linwei Tao, Minjing Dong, Chang Xu:
Dual Focal Loss for Calibration. 33833-33849
		Stone Tao, Xiaochen Li, Tongzhou Mu, Zhiao Huang, Yuzhe Qin, Hao Su:
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization. 33850-33882
		Rohan Taori, Tatsunori Hashimoto:
Data Feedback Loops: Model-driven Amplification of Dataset Biases. 33883-33920
		Ayush Kumar Tarun, Vikram Singh Chundawat, Murari Mandal, Mohan S. Kankanhalli:
Deep Regression Unlearning. 33921-33939
		Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam:
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. 33940-33960
		Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer:
Concurrent Shuffle Differential Privacy Under Continual Observation. 33961-33982
		Jiaye Teng, Bohang Zhang, Ruichen Li, Haowei He, Yequan Wang, Yan Tian, Yang Yuan:
Finding Generalization Measures by Contrasting Signal and Noise. 33983-34010
		Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History Dependent Dynamic Contexts. 34011-34053
		Lucile Ter-Minassian, Oscar Clivio, Karla DiazOrdaz, Robin J. Evans, Christopher C. Holmes:
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. 34054-34089
		Ashutosh Tewari:
On the Estimation of Gaussian Mixture Copula Models. 34090-34104
		Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Target-Aware Generative Augmentations for Single-Shot Adaptation. 34105-34119
		Qinglong Tian, Xin Zhang, Jiwei Zhao:
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models. 34120-34142
		Louis C. Tiao, Vincent Dutordoir, Victor Picheny:
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. 34143-34160
		Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. 34161-34221
		Alexandru Tifrea, Jacob Clarysse, Fanny Yang:
Margin-based sampling in high dimensions: When being active is less efficient than staying passive. 34222-34262
		Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. 34263-34279
		Malik Tiomoko, Romain Couillet, Frédéric Pascal:
PCA-based Multi-Task Learning: a Random Matrix Approach. 34280-34300
		Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed:
Perturbation Analysis of Neural Collapse. 34301-34329
		Rishabh Tiwari, Pradeep Shenoy:
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve. 34330-34343
		Christian Tomani, Futa Kai Waseda, Yuesong Shen, Daniel Cremers:
Beyond In-Domain Scenarios: Robust Density-Aware Calibration. 34344-34368
		Peifeng Tong, Wu Su, He Li, Jialin Ding, Zhan Haoxiang, Song Xi Chen:
Distribution Free Domain Generalization. 34369-34378
		Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens:
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. 34379-34393
		Francesco Tonolini, Nikolaos Aletras, Yunlong Jiao, Gabriella Kazai:
Robust Weak Supervision with Variational Auto-Encoders. 34394-34408
		Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone:
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. 34409-34430
		Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. 34431-34455
		Asher Trockman, J. Zico Kolter:
Mimetic Initialization of Self-Attention Layers. 34456-34468
		Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. 34469-34490
		Hanna Tseran, Guido Montúfar:
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. 34491-34532
		Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Networks Training. 34533-34555
		Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. 34556-34583
		Rajan Udwani:
Submodular Order Functions and Assortment Optimization. 34584-34614
		Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. 34615-34641
		Enayat Ullah, Raman Arora:
From Adaptive Query Release to Machine Unlearning. 34642-34667
		Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh:
Private Federated Learning with Autotuned Compression. 34668-34708
		Théo Uscidda, Marco Cuturi:
The Monge Gap: A Regularizer to Learn All Transport Maps. 34709-34733
		Adrien Vacher, François-Xavier Vialard:
Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm. 34734-34758
		Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz:
Random Grid Neural Processes for Parametric Partial Differential Equations. 34759-34778
		Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke:
Delayed Feedback in Kernel Bandits. 34779-34792
		Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. 34793-34808
		Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicolò Cesa-Bianchi:
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts. 34809-34830
		Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke:
Causal Isotonic Calibration for Heterogeneous Treatment Effects. 34831-34854
		Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. 34855-34874
		Filippo Vannella, Alexandre Proutière, Jaeseong Jeong:
Best Arm Identification in Multi-Agent Multi-Armed Bandits. 34875-34907
		Harshit Varma, Abhijeet Awasthi, Sunita Sarawagi:
Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models. 34908-34923
		Nate Veldt:
Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs. 34924-34951
		Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou:
Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. 34952-34977
		Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury:
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. 34978-35005
		Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco:
Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs. 35006-35023
		David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. 35024-35036
		Yogesh Verma, Markus Heinonen, Vikas Garg:
AbODE: Ab initio antibody design using conjoined ODEs. 35037-35050
		Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
TabLeak: Tabular Data Leakage in Federated Learning. 35051-35083
		Paul Vicol:
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. 35084-35119
		Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai:
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. 35120-35136
		Cameron Voloshin, Abhinav Verma, Yisong Yue:
Eventual Discounting Temporal Logic Counterfactual Experience Replay. 35137-35150
		Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers Learn In-Context by Gradient Descent. 35151-35174
		Julius von Rohrscheidt, Bastian Rieck:
Topological Singularity Detection at Multiple Scales. 35175-35197
		Václav Vorácek, Matthias Hein:
Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints. 35198-35222
		Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung:
Vector Quantized Wasserstein Auto-Encoder. 35223-35242
		Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli:
Competitive Gradient Optimization. 35243-35276
		Nikhil Vyas, Sham M. Kakade, Boaz Barak:
On Provable Copyright Protection for Generative Models. 35277-35299
		Andrew Wagenmaker, Aldo Pacchiano:
Leveraging Offline Data in Online Reinforcement Learning. 35300-35338
		Tal Wagner, Yonatan Naamad, Nina Mishra:
Fast Private Kernel Density Estimation via Locality Sensitive Quantization. 35339-35367
		Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick:
Investigating the Role of Model-Based Learning in Exploration and Transfer. 35368-35383
		Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad, David Güera, Zhile Ren, Qi Shan:
UPSCALE: Unconstrained Channel Pruning. 35384-35412
		Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein:
Poisoning Language Models During Instruction Tuning. 35413-35425
		Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan:
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. 35426-35443
		Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song:
Multiplier Bootstrap-based Exploration. 35444-35490
		Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang:
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits. 35491-35524
		Chen Wang:
Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits. 35525-35547
		Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du:
Improved Active Multi-Task Representation Learning via Lasso. 35548-35578
		Yingjie Wang, Hong Chen, Weifeng Liu, Fengxiang He, Tieliang Gong, Youcheng Fu, Dacheng Tao:
Tilted Sparse Additive Models. 35579-35604
		Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf:
From Hypergraph Energy Functions to Hypergraph Neural Networks. 35605-35623
		Shaoru Wang, Jin Gao, Zeming Li, Xiaoqin Zhang, Weiming Hu:
A Closer Look at Self-Supervised Lightweight Vision Transformers. 35624-35641
		Haibin Wang, Ce Ge, Hesen Chen, Xiuyu Sun:
PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search. 35642-35654
		Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Superhuman Go AIs. 35655-35739
		Kaifu Wang, Hangfeng He, Tin D. Nguyen, Piyush Kumar, Dan Roth:
On Regularization and Inference with Label Constraints. 35740-35762
		Qiuhao Wang, Chin Pang Ho, Marek Petrik:
Policy Gradient in Robust MDPs with Global Convergence Guarantee. 35763-35797
		Ziming Wang, Runhao Jiang, Shuang Lian, Rui Yan, Huajin Tang:
Adaptive Smoothing Gradient Learning for Spiking Neural Networks. 35798-35816
		Yansen Wang, Xinyang Jiang, Kan Ren, Caihua Shan, Xufang Luo, Dongqi Han, Kaitao Song, Yifei Shen, Dongsheng Li:
CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling. 35817-35835
		Xiaoyu Wang, Mikael Johansson, Tong Zhang:
Generalized Polyak Step Size for First Order Optimization with Momentum. 35836-35863
		Kaiwen Wang, Nathan Kallus, Wen Sun:
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. 35864-35907
		Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li:
FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. 35908-35948
		Jialu Wang, Ping Li, Feifang Hu:
A/B Testing in Network Data with Covariate-Adaptive Randomization. 35949-35969
		Andrew Wang, Andrew C. Li, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith:
Learning Belief Representations for Partially Observable Deep RL. 35970-35988
		Hang Wang, Sen Lin, Junshan Zhang:
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap. 35989-36019
		Yanbo Wang, Letao Liu, Justin Dauwels:
Slot-VAE: Object-Centric Scene Generation with Slot Attention. 36020-36035
		Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Ben Hu:
DIVISION: Memory Efficient Training via Dual Activation Precision. 36036-36057
		Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang:
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. 36058-36076
		Hongyu Wang, Shuming Ma, Shaohan Huang, Li Dong, Wenhui Wang, Zhiliang Peng, Yu Wu, Payal Bajaj, Saksham Singhal, Alon Benhaim, Barun Patra, Zhun Liu, Vishrav Chaudhary, Xia Song, Furu Wei:
Magneto: A Foundation Transformer. 36077-36092
		Ruigang Wang, Ian R. Manchester:
Direct Parameterization of Lipschitz-Bounded Deep Networks. 36093-36110
		Ziqiao Wang, Yongyi Mao:
Tighter Information-Theoretic Generalization Bounds from Supersamples. 36111-36137
		Jianfeng Wang, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Thomas Lukasiewicz:
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation. 36138-36156
		Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen:
GC-Flow: A Graph-Based Flow Network for Effective Clustering. 36157-36173
		Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, Wenwu Zhu:
Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation. 36174-36192
		Peihao Wang, Rameswar Panda, Zhangyang Wang:
Data Efficient Neural Scaling Law via Model Reusing. 36193-36204
		Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu:
Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. 36205-36223
		Aoran Wang, Jun Pang:
Active Learning based Structural Inference. 36224-36245
		Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan:
Better Diffusion Models Further Improve Adversarial Training. 36246-36263
		Qingyang Wang, Michael Alan Powell, Eric W. Bridgeford, Ali Geisa, Joshua T. Vogelstein:
Polarity Is All You Need to Learn and Transfer Faster. 36264-36284
		Jinxin Wang, Yuen-Man Pun, Xiaolu Wang, Peng Wang, Anthony Man-Cho So:
Projected Tensor Power Method for Hypergraph Community Recovery. 36285-36307
		Tian-Zuo Wang, Tian Qin, Zhi-Hua Zhou:
Estimating Possible Causal Effects with Latent Variables via Adjustment. 36308-36335
		Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov:
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. 36336-36354
		Jitao Wang, Chengchun Shi, Zhenke Wu:
A Robust Test for the Stationarity Assumption in Sequential Decision Making. 36355-36379
		Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai:
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. 36380-36390
		Aoran Wang, Tsz Pan Tong, Jun Pang:
Effective and Efficient Structural Inference with Reservoir Computing. 36391-36410
		Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang:
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. 36411-36430
		Yue Wang, Alvaro Velasquez, George K. Atia, Ashley Prater-Bennette, Shaofeng Zou:
Model-Free Robust Average-Reward Reinforcement Learning. 36431-36469
		Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang:
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy. 36470-36493
		Qian Wang, Zongjun Yang, Xiaotie Deng, Yuqing Kong:
Learning to Bid in Repeated First-Price Auctions with Budgets. 36494-36513
		Xiaolu Wang, Chung-Yiu Yau, Hoi-To Wai:
Network Effects in Performative Prediction Games. 36514-36540
		Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning a Single Neuron via Sharpness. 36541-36577
		Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, Jennifer G. Dy:
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning. 36578-36592
		Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu:
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. 36593-36604
		Runzhong Wang, Yunhao Zhang, Ziao Guo, Tianyi Chen, Xiaokang Yang, Junchi Yan:
LinSATNet: The Positive Linear Satisfiability Neural Networks. 36605-36625
		Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang, Liwei Wang, Chongjie Zhang:
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation. 36626-36669
		Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang:
Reachability-Aware Laplacian Representation in Reinforcement Learning. 36670-36693
		Kaixin Wang, Daquan Zhou, Jiashi Feng, Shie Mannor:
PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient. 36694-36713
		Richard A. Watson, Hengrui Cai, Xinming An, Samuel A. McLean, Rui Song:
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs. 36714-36747
		Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas:
Nonparametric Extensions of Randomized Response for Private Confidence Sets. 36748-36789
		Melanie Weber, Suvrit Sra:
Global optimality for Euclidean CCCP under Riemannian convexity. 36790-36803
		Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. 36804-36820
		Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He:
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. 36821-36838
		Chunyu Wei, Yu Wang, Bing Bai, Kai Ni, David Brady, Lu Fang:
Boosting Graph Contrastive Learning via Graph Contrastive Saliency. 36839-36855
		Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang:
Set-membership Belief State-based Reinforcement Learning for POMDPs. 36856-36867
		Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li:
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. 36868-36886
		Christian Dietrich Weilbach, William Harvey, Frank Wood:
Graphically Structured Diffusion Models. 36887-36909
		Pascal Welke, Maximilian Thiessen, Fabian Jogl, Thomas Gärtner:
Expectation-Complete Graph Representations with Homomorphisms. 36910-36925
		Jeffrey Wen, Rizwan Ahmad, Philip Schniter:
A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging. 36926-36939
		Haitao Wen, Haoyang Cheng, Heqian Qiu, Lanxiao Wang, Lili Pan, Hongliang Li:
Optimizing Mode Connectivity for Class Incremental Learning. 36940-36957
		Yijia Weng, Kaichun Mo, Ruoxi Shi, Yanchao Yang, Leonidas J. Guibas:
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks. 36958-36977
		Zejia Weng, Xitong Yang, Ang Li, Zuxuan Wu, Yu-Gang Jiang:
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization. 36978-36989
		Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu:
Fully-Adaptive Composition in Differential Privacy. 36990-37007
		Jeffrey Willette, Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation. 37008-37041
		Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie:
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning. 37042-37065
		Daniel J. Williams, Song Liu:
Approximate Stein Classes for Truncated Density Estimation. 37066-37090
		Rick Wilming, Leo Kieslich, Benedict Clark, Stefan Haufe:
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables. 37091-37107
		David Wipf:
Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations. 37108-37132
		Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. 37133-37156
		Jiin Woo, Gauri Joshi, Yuejie Chi:
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. 37157-37216
		Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi:
Learning Deep Time-index Models for Time Series Forecasting. 37217-37237
		David P. Woodruff, Taisuke Yasuda:
Sharper Bounds for ℓp Sensitivity Sampling. 37238-37272
		Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. 37273-37292
		Junran Wu, Xueyuan Chen, Bowen Shi, Shangzhe Li, Ke Xu:
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning. 37293-37312
		Zhengxuan Wu, Karel D'Oosterlinck, Atticus Geiger, Amir Zur, Christopher Potts:
Causal Proxy Models for Concept-based Model Explanations. 37313-37334
		Xiaobao Wu, Xinshuai Dong, Thong Thanh Nguyen, Anh Tuan Luu:
Effective Neural Topic Modeling with Embedding Clustering Regularization. 37335-37357
		Bin Wu, Jinyuan Fang, Xiangxiang Zeng, Shangsong Liang, Qiang Zhang:
Adaptive Compositional Continual Meta-Learning. 37358-37378
		Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao:
Anchor Sampling for Federated Learning with Partial Client Participation. 37379-37416
		Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long:
Solving High-Dimensional PDEs with Latent Spectral Models. 37417-37438
		Yihan Wu, Heng Huang, Hongyang Zhang:
A Law of Robustness beyond Isoperimetry. 37439-37455
		Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Uncovering Adversarial Risks of Test-Time Adaptation. 37456-37495
		Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu:
Stable Estimation of Heterogeneous Treatment Effects. 37496-37510
		Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li:
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching. 37511-37523
		Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He:
Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases. 37524-37539
		Guoqiang Wu, Chongxuan Li, Yilong Yin:
Towards Understanding Generalization of Macro-AUC in Multi-label Learning. 37540-37570
		Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li:
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs. 37571-37581
		Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson:
Delay-agnostic Asynchronous Coordinate Update Algorithm. 37582-37606
		Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran:
Masked Trajectory Models for Prediction, Representation, and Control. 37607-37623
		Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. 37624-37634
		Hao Wu, Olga Ohrimenko, Anthony Wirth:
Tight Data Access Bounds for Private Top-k Selection. 37635-37655
		Lei Wu, Weijie J. Su:
The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent. 37656-37684
		Runzhe Wu, Masatoshi Uehara, Wen Sun:
Distributional Offline Policy Evaluation with Predictive Error Guarantees. 37685-37712
		Chengyue Wu, Teng Wang, Yixiao Ge, Zeyu Lu, Ruisong Zhou, Ying Shan, Ping Luo:
π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation. 37713-37727
		Changlong Wu, Yifan Wang, Ananth Grama, Wojciech Szpankowski:
Learning Functional Distributions with Private Labels. 37728-37744
		Wenjie Wu, Ge Yan, Xudong Lu, Kaisen Pan, Junchi Yan:
QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms. 37745-37764
		Shirley Wu, Mert Yüksekgönül, Linjun Zhang, James Zou:
Discover and Cure: Concept-aware Mitigation of Spurious Correlation. 37765-37786
		David Xing Wu, Chulhee Yun, Suvrit Sra:
On the Training Instability of Shuffling SGD with Batch Normalization. 37787-37845
		Jiawei Wu, Changqing Zhang, Zuoyong Li, Huazhu Fu, Xi Peng, Joey Tianyi Zhou:
dugMatting: Decomposed-Uncertainty-Guided Matting. 37846-37859
		Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng:
Personalized Federated Learning under Mixture of Distributions. 37860-37879
		Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang:
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. 37880-37918
		Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. 37919-37951
		Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding:
Understanding Backdoor Attacks through the Adaptability Hypothesis. 37952-37976
		Ruicheng Xian, Lang Yin, Han Zhao:
Fair and Optimal Classification via Post-Processing. 37977-38012
		Zhen Xiang, Zidi Xiong, Bo Li:
UMD: Unsupervised Model Detection for X2X Backdoor Attacks. 38013-38038
		Jie Xiao, Xueyang Fu, Man Zhou, Hongjian Liu, Zheng-Jun Zha:
Random Shuffle Transformer for Image Restoration. 38039-38058
		Peiyao Xiao, Kaiyi Ji:
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. 38059-38086
		Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, Song Han:
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. 38087-38099
		Wei Xiao, Tsun-Hsuan Wang, Ramin M. Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus:
On the Forward Invariance of Neural ODEs. 38100-38124
		Jinqi Xiao, Miao Yin, Yu Gong, Xiao Zang, Jian Ren, Bo Yuan:
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models. 38125-38136
		Pengtao Xie:
Improving Bi-level Optimization Based Methods with Inspiration from Humans' Classroom Study Techniques. 38137-38186
		Zhihui Xie, Zichuan Lin, Deheng Ye, Qiang Fu, Yang Wei, Shuai Li:
Future-conditioned Unsupervised Pretraining for Decision Transformer. 38187-38203
		Liangbin Xie, Xintao Wang, Xiangyu Chen, Gen Li, Ying Shan, Jiantao Zhou, Chao Dong:
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models. 38204-38226
		Chuhan Xie, Wenhao Yang, Zhihua Zhang:
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes. 38227-38257
		Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li, Mohamed E. Hussein, Wael AbdAlmageed:
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment. 38258-38271
		Dong Xing, Pengjie Gu, Qian Zheng, Xinrun Wang, Shanqi Liu, Longtao Zheng, Bo An, Gang Pan:
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification. 38272-38285
		Zheng Xiong, Jacob Beck, Shimon Whiteson:
Universal Morphology Control via Contextual Modulation. 38286-38300
		Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Relevant Walk Search for Explaining Graph Neural Networks. 38301-38324
		Frank F. Xu, Uri Alon, Graham Neubig:
Why do Nearest Neighbor Language Models Work? 38325-38341
		Zuheng Xu, Naitong Chen, Trevor Campbell:
MixFlows: principled variational inference via mixed flows. 38342-38376
		Tongda Xu, Han Gao, Chenjian Gao, Yuanyuan Wang, Dailan He, Jinyong Pi, Jixiang Luo, Ziyu Zhu, Mao Ye, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang:
Bit Allocation using Optimization. 38377-38399
		Wenhao Xu, Xuefeng Gao, Xuedong He:
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents. 38400-38427
		Han Xu, Pengfei He, Jie Ren, Yuxuan Wan, Zitao Liu, Hui Liu, Jiliang Tang:
Probabilistic Categorical Adversarial Attack and Adversarial Training. 38428-38442
		Xiang Xu, Pradeep Kumar Jayaraman, Joseph George Lambourne, Karl D. D. Willis, Yasutaka Furukawa:
Hierarchical Neural Coding for Controllable CAD Model Generation. 38443-38461
		Hainan Xu, Fei Jia, Somshubra Majumdar, He Huang, Shinji Watanabe, Boris Ginsburg:
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations. 38462-38484
		Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones:
Constrained Efficient Global Optimization of Expensive Black-box Functions. 38485-38498
		Mengfan Xu, Diego Klabjan:
Pareto Regret Analyses in Multi-objective Multi-armed Bandit. 38499-38517
		Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu:
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. 38518-38534
		Jing Xu, Haoxiong Liu:
Quantifying the Variability Collapse of Neural Networks. 38535-38550
		Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng:
Progressive Purification for Instance-Dependent Partial Label Learning. 38551-38565
		Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola:
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. 38566-38591
		Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec:
Geometric Latent Diffusion Models for 3D Molecule Generation. 38592-38610
		Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma:
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing. 38611-38654
		Hongzuo Xu, Yijie Wang, Juhui Wei, Songlei Jian, Yizhou Li, Ning Liu:
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. 38655-38673
		Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui:
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. 38674-38706
		Chen Xu, Yao Xie:
Sequential Predictive Conformal Inference for Time Series. 38707-38727
		Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou:
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video. 38728-38748
		Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang:
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. 38749-38767
		Yunbei Xu, Assaf Zeevi:
Bayesian Design Principles for Frequentist Sequential Learning. 38768-38800
		Hang Xu, Wenxuan Zhang, Jiawei Fei, Yuzhe Wu, Tingwen Xie, Jun Huang, Yuchen Xie, Mohamed Elhoseiny, Panos Kalnis:
SLAMB: Accelerated Large Batch Training with Sparse Communication. 38801-38825
		Peng Xu, Lin Zhang, Xuanzhou Liu, Jiaqi Sun, Yue Zhao, Haiqin Yang, Bei Yu:
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks. 38826-38847
		Yang Xu, Jin Zhu, Chengchun Shi, Shikai Luo, Rui Song:
An Instrumental Variable Approach to Confounded Off-Policy Evaluation. 38848-38880
		Yecheng Xue, Xiaoyu Chen, Tongyang Li, Shaofeng H.-C. Jiang:
Near-Optimal Quantum Coreset Construction Algorithms for Clustering. 38881-38912
		Fuzhao Xue, Jianghai Chen, Aixin Sun, Xiaozhe Ren, Zangwei Zheng, Xiaoxin He, Yongming Chen, Xin Jiang, Yang You:
A Study on Transformer Configuration and Training Objective. 38913-38925
		Rui Xue, Haoyu Han, MohamadAli Torkamani, Jian Pei, Xiaorui Liu:
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation. 38926-38937
		Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, Baharan Mirzasoleiman:
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression. 38938-38970
		Fuzhao Xue, Valerii Likhosherstov, Anurag Arnab, Neil Houlsby, Mostafa Dehghani, Yang You:
Adaptive Computation with Elastic Input Sequence. 38971-38988
		Taku Yamagata, Ahmed Khalil, Raúl Santos-Rodríguez:
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL. 38989-39007
		Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa, Sho Sonoda:
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation. 39008-39034
		Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, Yangyang Xu:
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. 39035-39061
		Wilson Yan, Danijar Hafner, Stephen James, Pieter Abbeel:
Temporally Consistent Transformers for Video Generation. 39062-39098
		Zhiqiang Yan, Xiang Li, Kun Wang, Shuo Chen, Jun Li, Jian Yang:
Distortion and Uncertainty Aware Loss for Panoramic Depth Completion. 39099-39109
		Jingquan Yan, Hao Wang:
Self-Interpretable Time Series Prediction with Counterfactual Explanations. 39110-39125
		Ge Yan, Huaijin Wu, Junchi Yan:
Quantum 3D Graph Learning with Applications to Molecule Embedding. 39126-39137
		Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou:
Fast Rates in Time-Varying Strongly Monotone Games. 39138-39164
		Hiroki Yanagisawa:
Proper Scoring Rules for Survival Analysis. 39165-39182
		Rushuai Yang, Chenjia Bai, Hongyi Guo, Siyuan Li, Bin Zhao, Zhen Wang, Peng Liu, Xuelong Li:
Behavior Contrastive Learning for Unsupervised Skill Discovery. 39183-39204
		Junwen Yang, Yifan Feng:
Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback. 39205-39233
		Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi:
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering. 39234-39251
		Shenghao Yang, Kimon Fountoulakis:
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees. 39252-39276
		Soojung Yang, Rafael Gómez-Bombarelli:
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins. 39277-39298
		Ziqing Yang, Xinlei He, Zheng Li, Michael Backes, Mathias Humbert, Pascal Berrang, Yang Zhang:
Data Poisoning Attacks Against Multimodal Encoders. 39299-39313
		Yu Yang, Hao Kang, Baharan Mirzasoleiman:
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. 39314-39330
		Dongyoon Yang, Insung Kong, Yongdai Kim:
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples. 39331-39348
		Zonghan Yang, Peng Li, Tianyu Pang, Yang Liu:
Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics. 39349-39364
		Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman:
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning. 39365-39379
		Adam X. Yang, Maxime Robeyns, Edward Milsom, Ben Anson, Nandi Schoots, Laurence Aitchison:
A theory of representation learning gives a deep generalisation of kernel methods. 39380-39415
		Joonhyuk Yang, Dongpil Shin, Hye Won Chung:
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation. 39416-39452
		Yongyi Yang, Jacob Steinhardt, Wei Hu:
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations. 39453-39487
		Jianke Yang, Robin Walters, Nima Dehmamy, Rose Yu:
Generative Adversarial Symmetry Discovery. 39488-39508
		Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, Gao Huang:
Boosting Offline Reinforcement Learning with Action Preference Query. 39509-39523
		Jianan Yang, Haobo Wang, Sai Wu, Gang Chen, Junbo Zhao:
Towards Controlled Data Augmentations for Active Learning. 39524-39542
		Rui Yang, Lin Yong, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang:
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? 39543-39571
		Lingxiao Yang, Hongzhi You, Zonglei Zhen, Dahui Wang, Xiaohong Wan, Xiaohua Xie, Ru-Yuan Zhang:
Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests. 39572-39583
		Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi:
Change is Hard: A Closer Look at Subpopulation Shift. 39584-39622
		Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi:
Continual Task Allocation in Meta-Policy Network via Sparse Prompting. 39623-39638
		Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King:
Hyperbolic Representation Learning: Revisiting and Advancing. 39639-39659
		Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu:
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? 39660-39673
		Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu:
How Bad is Top-K Recommendation under Competing Content Creators? 39674-39701
		Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu:
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. 39702-39721
		Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. 39722-39754
		Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-Tau Yih:
Retrieval-Augmented Multimodal Language Modeling. 39755-39769
		Haotian Ye, Xiaoyu Chen, Liwei Wang, Simon Shaolei Du:
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness. 39770-39800
		Rui Ye, Zhenyang Ni, Fangzhao Wu, Siheng Chen, Yanfeng Wang:
Personalized Federated Learning with Inferred Collaboration Graphs. 39801-39817
		Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong:
Compositional Exemplars for In-context Learning. 39818-39833
		Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. 39834-39863
		Huigen Ye, Hua Xu, Hongyan Wang, Chengming Wang, Yu Jiang:
GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming. 39864-39878
		Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu, Siheng Chen, Yanfeng Wang:
FedDisco: Federated Learning with Discrepancy-Aware Collaboration. 39879-39902
		Xinyu Ye, Ge Yan, Junchi Yan:
Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver. 39903-39912
		Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova:
Temporal Label Smoothing for Early Event Prediction. 39913-39938
		Raanan Y. Yehezkel Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik:
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders. 39939-39950
		Jialin Yi, Milan Vojnovic:
Doubly Adversarial Federated Bandits. 39951-39967
		Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Yunkai Gao, Kaizhao Yuan, Ruizhi Chen, Siming Lan, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen:
Online Prototype Alignment for Few-shot Policy Transfer. 39968-39983
		Mingxuan Yi, Zhanxing Zhu, Song Liu:
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows. 39984-40000
		Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. 40001-40039
		Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo:
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification. 40040-40053
		Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Adaptive Estimation of Graphical Models under Total Positivity. 40054-40074
		Seungryong Yoo, Eunji Kim, Dahuin Jung, Jungbeom Lee, Sungroh Yoon:
Improving Visual Prompt Tuning for Self-supervised Vision Transformers. 40075-40092
		Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo, Inseop Chung, Nojun Kwak:
End-to-End Multi-Object Detection with a Regularized Mixture Model. 40093-40110
		Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee, Minchan Kim, Seok Min Kim, Nam Soo Kim:
EM-Network: Oracle Guided Self-distillation for Sequence Learning. 40111-40128
		Jaehong Yoon, Sung Ju Hwang, Yue Cao:
Continual Learners are Incremental Model Generalizers. 40129-40146
		Jaesik Yoon, Yi-Fu Wu, Heechul Bae, Sungjin Ahn:
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning. 40147-40174
		Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov:
Graph Generative Model for Benchmarking Graph Neural Networks. 40175-40198
		Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu:
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. 40199-40224
		Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki:
Entropy-driven Unsupervised Keypoint Representation Learning in Videos. 40225-40253
		Kenny John Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. 40254-40276
		Anlan Yu, Ning Lyu, Jieming Yin, Zhiyuan Yan, Wujie Wen:
COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects. 40277-40289
		Chenglin Yu, Xinsong Ma, Weiwei Liu:
Delving into Noisy Label Detection with Clean Data. 40290-40305
		Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan:
Bag of Tricks for Training Data Extraction from Language Models. 40306-40320
		Dayou Yu, Weishi Shi, Qi Yu:
Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. 40321-40338
		Jiashuo Yu, Yaohui Wang, Xinyuan Chen, Xiao Sun, Yu Qiao:
Long-Term Rhythmic Video Soundtracker. 40339-40353
		Lijia Yu, Yihan Wang, Xiao-Shan Gao:
Adversarial Parameter Attack on Deep Neural Networks. 40354-40372
		Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao:
CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models. 40373-40389
		Liren Yu, Jiaming Xu, Xiaojun Lin:
SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching. 40390-40411
		Haiyang Yu, Zhao Xu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji:
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian. 40412-40424
		Xian Yu, Lei Ying:
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures. 40425-40451
		Zishun Yu, Xinhua Zhang:
Actor-Critic Alignment for Offline-to-Online Reinforcement Learning. 40452-40474
		Zhongzhi Yu, Yang Zhang, Kaizhi Qian, Cheng Wan, Yonggan Fu, Yongan Zhang, Yingyan Celine Lin:
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning. 40475-40487
		Ganzhao Yuan:
Coordinate Descent Methods for Fractional Minimization. 40488-40518
		Yang Yuan:
On the Power of Foundation Models. 40519-40530
		Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng:
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning. 40531-40554
		Eunggu Yun, Hyungi Lee, Giung Nam, Juho Lee:
Traversing Between Modes in Function Space for Fast Ensembling. 40555-40577
		Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. 40578-40604
		Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi:
KDEformer: Accelerating Transformers via Kernel Density Estimation. 40605-40623
		Santiago Zanella Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones:
Bayesian Estimation of Differential Privacy. 40624-40636
		Andrea Zanette:
When is Realizability Sufficient for Off-Policy Reinforcement Learning? 40637-40668
		Sepanta Zeighami, Cyrus Shahabi:
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing. 40669-40680
		Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard:
Sequential Counterfactual Risk Minimization. 40681-40706
		Zhanpeng Zeng, Michael Davies, Pranav Pulijala, Karthikeyan Sankaralingam, Vikas Singh:
LookupFFN: Making Transformers Compute-lite for CPU inference. 40707-40718
		Shiwei Zeng, Jie Shen:
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise. 40719-40748
		Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, Hanghang Tong:
Generative Graph Dictionary Learning. 40749-40769
		Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge, Jason Ramapuram, Yizhe Zhang, Jiatao Gu, Joshua M. Susskind:
Stabilizing Transformer Training by Preventing Attention Entropy Collapse. 40770-40803
		Yuheng Zhang, Yu Bai, Nan Jiang:
Offline Learning in Markov Games with General Function Approximation. 40804-40829
		Jianyu Zhang, Léon Bottou:
Learning useful representations for shifting tasks and distributions. 40830-40850
		Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Iterative Machine Teaching. 40851-40870
		Cindy Y. Zhang, Sarah Huiyi Cen, Devavrat Shah:
Matrix Estimation for Individual Fairness. 40871-40887
		Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, Dinghao Wu:
Graph Contrastive Backdoor Attacks. 40888-40910
		Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao:
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. 40911-40931
		Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck:
Tractable Control for Autoregressive Language Generation. 40932-40945
		Yuhua Zhang, Walter H. Dempsey:
CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data. 40946-40975
		Jiuling Zhang, Zhiming Ding:
Rethink DARTS Search Space and Renovate a New Benchmark. 40976-40995
		Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm:
Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization. 40996-41018
		Bohang Zhang, Guhao Feng, Yiheng Du, Di He, Liwei Wang:
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests. 41019-41077
		Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, Wenming Yang:
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution. 41078-41091
		Biao Zhang, Barry Haddow, Alexandra Birch:
Prompting Large Language Model for Machine Translation: A Case Study. 41092-41110
		Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu:
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. 41111-41132
		Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang:
When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations. 41133-41150
		Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Siu Ming Yiu, Ruihua Han:
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation. 41151-41163
		Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang:
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. 41164-41193
		Jun Zhang, Shuyang Jiang, Jiangtao Feng, Lin Zheng, Lingpeng Kong:
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling. 41194-41218
		Shenao Zhang, Wanxin Jin, Zhaoran Wang:
Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics. 41219-41243
		Hang Zhang, Ping Li:
One-Step Estimator for Permuted Sparse Recovery. 41244-41267
		Chenyi Zhang, Tongyang Li:
Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions. 41268-41299
		Xinlu Zhang, Shiyang Li, Zhiyu Chen, Xifeng Yan, Linda Ruth Petzold:
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling. 41300-41313
		Hao Zhang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong:
FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. 41314-41330
		Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin:
On the Optimality of Misspecified Kernel Ridge Regression. 41331-41353
		Jianyi Zhang, Ang Li, Minxue Tang, Jingwei Sun, Xiang Chen, Fan Zhang, Changyou Chen, Yiran Chen, Hai Li:
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. 41354-41381
		Zaixi Zhang, Qi Liu:
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design. 41382-41398
		Feilong Zhang, Xianming Liu, Shiyi Lin, Gang Wu, Xiong Zhou, Junjun Jiang, Xiangyang Ji:
No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. 41399-41413
		Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez:
The Wisdom of Hindsight Makes Language Models Better Instruction Followers. 41414-41428
		Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan:
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. 41429-41451
		Shijun Zhang, Jianfeng Lu, Hongkai Zhao:
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network. 41452-41487
		Yuxin Zhang, Yiting Luo, Mingbao Lin, Yunshan Zhong, Jingjing Xie, Fei Chao, Rongrong Ji:
Bi-directional Masks for Efficient N: M Sparse Training. 41488-41497
		Jie Zhang, Xiaosong Ma, Song Guo, Wenchao Xu:
Towards Unbiased Training in Federated Open-world Semi-supervised Learning. 41498-41509
		Shengping Zhang, Quanling Meng, Qinglin Liu, Liqiang Nie, Bineng Zhong, Xiaopeng Fan, Rongrong Ji:
Interactive Object Placement with Reinforcement Learning. 41510-41522
		Fangzhao Zhang, Mert Pilanci:
Optimal Shrinkage for Distributed Second-Order Optimization. 41523-41549
		Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi:
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. 41550-41578
		Fengxue Zhang, Jialin Song, James C. Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen:
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation. 41579-41595
		Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. 41596-41612
		Shangtong Zhang, Remi Tachet des Combes, Romain Laroche:
On the Convergence of SARSA with Linear Function Approximation. 41613-41646
		Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan:
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation. 41647-41676
		Qi Zhang, Yifei Wang, Yisen Wang:
On the Generalization of Multi-modal Contrastive Learning. 41677-41693
		Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. 41694-41714
		Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai:
Towards Trustworthy Explanation: On Causal Rationalization. 41715-41736
		He Zhang, Bang Wu, Shuo Wang, Xiangwen Yang, Minhui Xue, Shirui Pan, Xingliang Yuan:
Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks. 41737-41752
		Qingyang Zhang, Haitao Wu, Changqing Zhang, Qinghua Hu, Huazhu Fu, Joey Tianyi Zhou, Xi Peng:
Provable Dynamic Fusion for Low-Quality Multimodal Data. 41753-41769
		Kexun Zhang, Xianjun Yang, William Yang Wang, Lei Li:
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval. 41770-41785
		Qixin Zhang, Wenbing Ye, Zaiyi Chen, Haoyuan Hu, Enhong Chen, Yu Yang:
Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests. 41786-41818
		Xiaohui Zhang, Jiangyan Yi, Jianhua Tao, Chenglong Wang, Chu Yuan Zhang:
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection. 41819-41831
		Tianyi Zhang, Tao Yu, Tatsunori Hashimoto, Mike Lewis, Wen-Tau Yih, Daniel Fried, Sida Wang:
Coder Reviewer Reranking for Code Generation. 41832-41846
		Wanrong Zhang, Ruqi Zhang:
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. 41847-41860
		Yifan Zhang, Min-Ling Zhang:
Nearly-tight Bounds for Deep Kernel Learning. 41861-41879
		Tianping Zhang, Zheyu Aqa Zhang, Zhiyuan Fan, Haoyan Luo, Fengyuan Liu, Qian Liu, Wei Cao, Li Jian:
OpenFE: Automated Feature Generation with Expert-level Performance. 41880-41901
		Junkai Zhang, Weitong Zhang, Quanquan Gu:
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. 41902-41930
		Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek:
Unlocking Slot Attention by Changing Optimal Transport Costs. 41931-41951
		Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu:
Towards a Persistence Diagram that is Robust to Noise and Varied Densities. 41952-41972
		Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, Jiang Bian:
Robust Situational Reinforcement Learning in Face of Context Disturbances. 41973-41989
		Shaofeng Zhang, Qiang Zhou, Zhibin Wang, Fan Wang, Junchi Yan:
Patch-level Contrastive Learning via Positional Query for Visual Pre-training. 41990-41999
		Dora Zhao, Jerone Theodore Alexander Andrews, Alice Xiang:
Men Also Do Laundry: Multi-Attribute Bias Amplification. 42000-42017
		Xunyi Zhao, Théotime Le Hellard, Lionel Eyraud-Dubois, Julia Gusak, Olivier Beaumont:
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch. 42018-42045
		Yixiu Zhao, Scott W. Linderman:
Revisiting Structured Variational Autoencoders. 42046-42057
		Hao Zhao, Yuejiang Liu, Alexandre Alahi, Tao Lin:
On Pitfalls of Test-Time Adaptation. 42058-42080
		Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar:
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. 42081-42097
		Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, Weiming Zhang, Nenghai Yu:
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion. 42098-42109
		Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun:
Revisiting Simple Regret: Fast Rates for Returning a Good Arm. 42110-42158
		He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. 42159-42186
		Xuandong Zhao, Yu-Xiang Wang, Lei Li:
Protecting Language Generation Models via Invisible Watermarking. 42187-42199
		Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. 42200-42226
		Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen:
Simplified Temporal Consistency Reinforcement Learning. 42227-42246
		Liming Zhao, Kecheng Zheng, Yun Zheng, Deli Zhao, Jingren Zhou:
RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation. 42247-42258
		Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. 42259-42279
		Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Does Continual Learning Equally Forget All Parameters? 42280-42303
		Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. 42304-42316
		Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu:
Structure-informed Language Models Are Protein Designers. 42317-42338
		Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. 42339-42362
		Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu:
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs. 42363-42389
		Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar:
Fast Sampling of Diffusion Models via Operator Learning. 42390-42402
		Wenqing Zheng, S. P. Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, Zhangyang Wang:
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation. 42403-42419
		Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu:
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications. 42420-42477
		Ervine Zheng, Qi Yu:
Evidential Interactive Learning for Medical Image Captioning. 42478-42491
		Yizhen Zheng, He Zhang, Vincent Cheng-Siong Lee, Yu Zheng, Xiao Wang, Shirui Pan:
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs. 42492-42505
		Shengwei Zhou, Rufan Bai, Xiaowei Wu:
Multi-agent Online Scheduling: MMS Allocations for Indivisible Items. 42506-42516
		Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu:
Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration. 42517-42530
		Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David R. So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V. Le, Claire Cui, James Laudon, Jeff Dean:
Brainformers: Trading Simplicity for Efficiency. 42531-42542
		Mo Zhou, Rong Ge:
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. 42543-42573
		Zhi Zhou, Lan-Zhe Guo, Lin-Han Jia, Dingchu Zhang, Yu-Feng Li:
ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. 42574-42588
		Man Zhou, Jie Huang, Chun-Le Guo, Chongyi Li:
Fourmer: An Efficient Global Modeling Paradigm for Image Restoration. 42589-42601
		Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, Mrinmaya Sachan:
Controlled Text Generation with Natural Language Instructions. 42602-42613
		Tong Zhou, Yukui Luo, Shaolei Ren, Xiaolin Xu:
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation. 42614-42624
		Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon:
Deep Latent State Space Models for Time-Series Generation. 42625-42643
		Ziang Zhou, Jieming Shi, Renchi Yang, Yuanhang Zou, Qing Li:
SlotGAT: Slot-based Message Passing for Heterogeneous Graphs. 42644-42657
		Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh:
Fast Online Node Labeling for Very Large Graphs. 42658-42697
		Runlong Zhou, Ruosong Wang, Simon Shaolei Du:
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes. 42698-42723
		Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu:
Phase-aware Adversarial Defense for Improving Adversarial Robustness. 42724-42741
		Cai Zhou, Xiyuan Wang, Muhan Zhang:
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks. 42742-42768
		Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang:
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems. 42769-42789
		Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney:
A Three-regime Model of Network Pruning. 42790-42809
		Zihan Zhou, Tianshu Yu:
Learning to Decouple Complex Systems. 42810-42828
		Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang:
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation. 42829-42842
		Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han:
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. 42843-42877
		Runlong Zhou, Zihan Zhang, Simon Shaolei Du:
Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments. 42878-42914
		Sicheng Zhu, Bang An, Furong Huang, Sanghyun Hong:
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator. 42915-42937
		Harrison Zhu, Carles Balsells Rodas, Yingzhen Li:
Markovian Gaussian Process Variational Autoencoders. 42938-42961
		Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott:
Mixture Proportion Estimation Beyond Irreducibility. 42962-42982
		Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han:
Exploring Model Dynamics for Accumulative Poisoning Discovery. 42983-43004
		Tongtian Zhu, Fengxiang He, Kaixuan Chen, Mingli Song, Dacheng Tao:
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent. 43005-43036
		Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. 43037-43067
		Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han:
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. 43068-43104
		Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher:
Benign Overfitting in Deep Neural Networks under Lazy Training. 43105-43128
		Jiacheng Zhu, Jielin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao:
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics. 43129-43157
		Chaoyi Zhu, Stefanie Roos, Lydia Y. Chen:
LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. 43158-43180
		Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran:
XTab: Cross-table Pretraining for Tabular Transformers. 43181-43204
		Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang:
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. 43205-43227
		Junyi Zhu, Ruicong Yao, Matthew B. Blaschko:
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. 43228-43257
		Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li, Yang Liu:
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes. 43258-43288
		Dixian Zhu, Yiming Ying, Tianbao Yang:
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. 43289-43325
		Yubo Zhuang, Xiaohui Chen, Yun Yang:
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data. 43326-43346
		Juliusz Krysztof Ziomek, Haitham Bou-Ammar:
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? 43347-43368
		Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok:
Revisiting Bellman Errors for Offline Model Selection. 43369-43406
		Ziyin Liu, Zihao Wang:
spred: Solving L1 Penalty with SGD. 43407-43422
		Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
The Benefits of Mixup for Feature Learning. 43423-43479