TMLR 2023

611 papers

$f$-MICL: Understanding and Generalizing InfoNCE-Based Contrastive Learning Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu

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$k$-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien

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3D-Aware Video Generation Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc Van Gool, Radu Timofte

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A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores Naoya Takeishi, Yoshinobu Kawahara

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A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles Niklas Åkerblom, Morteza Haghir Chehreghani

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A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou

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A DNN Optimizer That Improves over AdaBelief by Suppression of the Adaptive Stepsize Range Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn

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A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification Alan Q. Wang, Mert R. Sabuncu

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A Free Lunch with Influence Functions? an Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation Matthew James Vowels, Sina Akbari, Necati Cihan Camgoz, Richard Bowden

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A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection Marine Picot, Federica Granese, Guillaume Staerman, Marco Romanelli, Francisco Messina, Pablo Piantanida, Pierre Colombo

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A Kernel Perspective on Behavioural Metrics for Markov Decision Processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

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A Measure of the Complexity of Neural Representations Based on Partial Information Decomposition David Alexander Ehrlich, Andreas Christian Schneider, Viola Priesemann, Michael Wibral, Abdullah Makkeh

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A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley A Fritz, Daniel Felsky, Michael Avidan, Yixin Chen, Christopher Ryan King

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A Probabilistic Taylor Expansion with Gaussian Processes Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä

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A Proximal Algorithm for Sampling Jiaming Liang, Yongxin Chen

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A Ranking Game for Imitation Learning Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum

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A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M Oberman

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A Revenue Function for Comparison-Based Hierarchical Clustering Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar

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A Robust Backpropagation-Free Framework for Images Timothy Zee, Alex Ororbia, Ankur Mali, Ifeoma Nwogu

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A Simulation Environment and Reinforcement Learning Method for Waste Reduction Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke

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A Stochastic Proximal Polyak Step Size Fabian Schaipp, Robert M. Gower, Michael Ulbrich

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A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning Fahad Sarfraz, Elahe Arani, Bahram Zonooz

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A Survey on Causal Discovery Methods for I.I.D. and Time Series Data Uzma Hasan, Emam Hossain, Md Osman Gani

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A Survey on the Possibilities & Impossibilities of AI-Generated Text Detection Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, Amrit Bedi

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A Survey on Transformers in Reinforcement Learning Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, Deheng Ye

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A Systematic Approach to Universal Random Features in Graph Neural Networks Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer

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A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann

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A Unified View of Masked Image Modeling Zhiliang Peng, Li Dong, Hangbo Bao, Furu Wei, Qixiang Ye

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A Variational Perspective on Generative Flow Networks Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A Naesseth

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About the Cost of Central Privacy in Density Estimation Clément Lalanne, Aurélien Garivier, Rémi Gribonval

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Accelerated Quality-Diversity Through Massive Parallelism Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully

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Accelerating Batch Active Learning Using Continual Learning Techniques Arnav Mohanty Das, Gantavya Bhatt, Megh Manoj Bhalerao, Vianne R. Gao, Rui Yang, Jeff Bilmes

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Achieving Risk Control in Online Learning Settings Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano

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Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits Zixin Zhong, Wang Chi Cheung, Vincent Tan

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Action Poisoning Attacks on Linear Contextual Bandits Guanlin Liu, Lifeng Lai

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Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task Jannik Kossen, Cătălina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave

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Active Learning of Ordinal Embeddings: A User Study on Football Data Christoffer Löffler, Kion Fallah, Stefano Fenu, Dario Zanca, Bjoern Eskofier, Christopher John Rozell, Christopher Mutschler

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Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko, Elnur Gasanov, Abdurakhmon Sadiev, Rustem Islamov, Peter Richtárik

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Adaptive Hyperparameter Selection for Differentially Private Gradient Descent Dominik Fay, Sindri Magnússon, Jens Sjölund, Mikael Johansson

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Adaptive Patch Foraging in Deep Reinforcement Learning Agents Nathan Wispinski, Andrew Butcher, Kory Wallace Mathewson, Craig S Chapman, Matthew Botvinick, Patrick M. Pilarski

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Addressing Caveats of Neural Persistence with Deep Graph Persistence Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata, A. Sophia Koepke

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Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness Yixin Wang, Dhanya Sridhar, David Blei

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Agent-State Construction with Auxiliary Inputs Ruo Yu Tao, Adam White, Marlos C. Machado

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AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takáč

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An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization Yutong Dai, Tianyi Chen, Guanyi Wang, Daniel Robinson

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An Analysis of Model-Based Reinforcement Learning from Abstracted Observations Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A Oliehoek

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An Explicit Expansion of the Kullback-Leibler Divergence Along Its Fisher-Rao Gradient Flow Carles Domingo-Enrich, Aram-Alexandre Pooladian

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An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms Abulikemu Abuduweili, Changliu Liu

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An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-MDP Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli

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Analysis of Convolutions, Non-Linearity and Depth in Graph Neural Networks Using Neural Tangent Kernel Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar

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Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng

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AP: Selective Activation for De-Sparsifying Pruned Networks Shiyu Liu, Rohan Ghosh, Mehul Motani

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Approximating Naive Bayes on Unlabelled Categorical Data Cormac Herley

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Assisted Learning for Organizations with Limited Imbalanced Data Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

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Assisting Human Decisions in Document Matching Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B Shah, Ameet Talwalkar

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Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration Kaspar Valk, Meelis Kull

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Asymptotic Analysis of Conditioned Stochastic Gradient Descent Rémi Leluc, François Portier

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Attacking Perceptual Similarity Metrics Abhijay Ghildyal, Feng Liu

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Attention Beats Concatenation for Conditioning Neural Fields Daniel Rebain, Mark J. Matthews, Kwang Moo Yi, Gopal Sharma, Dmitry Lagun, Andrea Tagliasacchi

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Attentional-Biased Stochastic Gradient Descent Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

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Augmented Language Models: A Survey Grégoire Mialon, Roberto Dessi, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom

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Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery Tony Liu, Patrick Lawlor, Lyle Ungar, Konrad Kording, Rahul Ladhania

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Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts Rohit Agarwal, Deepak Gupta, Alexander Horsch, Dilip K. Prasad

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Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning Yubei Chen, Adrien Bardes, Zengyi Li, Yann LeCun

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Bandwidth Enables Generalization in Quantum Kernel Models Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin

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Bayesian Causal Bandits with Backdoor Adjustment Prior Jireh Huang, Qing Zhou

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Bayesian Optimization with Informative Covariance Afonso Eduardo, Michael U. Gutmann

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Bayesian Quadrature for Neural Ensemble Search Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A Osborne

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Bayesian Transformed Gaussian Processes Xinran Zhu, Leo Huang, Eric Hans Lee, Cameron Alexander Ibrahim, David Bindel

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Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression Alexander Luke Ian Norcliffe, Lev Proleev, Diana Mincu, F Lee Hartsell, Katherine A Heller, Subhrajit Roy

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Benchmarks and Algorithms for Offline Preference-Based Reward Learning Daniel Shin, Anca Dragan, Daniel S. Brown

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Benchmarks for Physical Reasoning AI Andrew Melnik, Robin Schiewer, Moritz Lange, Andrei Ioan Muresanu, Mozhgan Saeidi, Animesh Garg, Helge Ritter

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Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence Kyle Matoba, Nikolaos Dimitriadis, François Fleuret

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Better Theory for SGD in the Nonconvex World Ahmed Khaled, Peter Richtárik

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Beyond Boundaries: A Novel Data-Augmentation Discourse for Open Domain Generalization Shirsha Bose, Ankit Jha, Hitesh Kandala, Biplab Banerjee

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Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich

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Beyond Information Gain: An Empirical Benchmark for Low-Switching-Cost Reinforcement Learning Shusheng Xu, Yancheng Liang, Yunfei Li, Simon Shaolei Du, Yi Wu

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Beyond Intuition: Rethinking Token Attributions Inside Transformers Jiamin Chen, Xuhong Li, Lei Yu, Dejing Dou, Haoyi Xiong

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Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Johan Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cesar Ferri, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Christopher Waites, Christian Voigt, Christopher D Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, C. Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germàn Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-Lopez, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Francis Anthony Shevlin, Hinrich Schuetze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh Dhole, Kevin Gimpel, Kevin Omondi, Kory Wallace Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros-Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje Ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramirez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael Andrew Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Andrew Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter W Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Russ Salakhutdinov, Ryan Andrew Chi, Seungjae Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel Stern Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Shammie Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven Piantadosi, Stuart Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsunori Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Venkatesh Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Sophie Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu

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Bidirectional View Based Consistency Regularization for Semi-Supervised Domain Adaptation Yuntao Du, 娟江, Hongtao Luo, Haiyang Yang, MingCai Chen, Chongjun Wang

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BIGRoC: Boosting Image Generation via a Robust Classifier Roy Ganz, Michael Elad

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Binary Classification Under Local Label Differential Privacy Using Randomized Response Mechanisms Shirong Xu, Chendi Wang, Will Wei Sun, Guang Cheng

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Black-Box Batch Active Learning for Regression Andreas Kirsch

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Black-Box Prompt Learning for Pre-Trained Language Models Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Lin Yong, Xiao Zhou, Tong Zhang

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Bounded Space Differentially Private Quantiles Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi

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Bounding Generalization Error with Input Compression: An Empirical Study with Infinite-Width Networks Angus Galloway, Anna Golubeva, Mahmoud Salem, Mihai Nica, Yani Ioannou, Graham W. Taylor

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Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling Junhyun Nam, Sangwoo Mo, Jaeho Lee, Jinwoo Shin

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Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata Patrick Soga, David Chiang

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Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale Botao Hao, Rahul Jain, Dengwang Tang, Zheng Wen

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Bridging Performance Gap Between Minimal and Maximal SVM Models Ondrej Such, René Fabricius

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Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies Shivakanth Sujit, Pedro Braga, Jorg Bornschein, Samira Ebrahimi Kahou

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Bridging the Gap Between Target Networks and Functional Regularization Alexandre Piché, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan

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Bridging the Sim2Real Gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting Viraj Uday Prabhu, David Acuna, Rafid Mahmood, Marc T. Law, Yuan-Hong Liao, Judy Hoffman, Sanja Fidler, James Lucas

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CAE V2: Context Autoencoder with CLIP Latent Alignment Xinyu Zhang, Jiahui Chen, Junkun Yuan, Qiang Chen, Jian Wang, Xiaodi Wang, Shumin Han, Xiaokang Chen, Jimin Pi, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang

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Calibrate and Debias Layer-Wise Sampling for Graph Convolutional Networks Yifan Chen, Tianning Xu, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu

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Calibrating and Improving Graph Contrastive Learning Ma Kaili, Garry Yang, Han Yang, Yongqiang Chen, James Cheng

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Can Pruning Improve Certified Robustness of Neural Networks? Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang

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Catastrophic Overfitting Can Be Induced with Discriminative Non-Robust Features Guillermo Ortiz-Jimenez, Pau de Jorge, Amartya Sanyal, Adel Bibi, Puneet K. Dokania, Pascal Frossard, Grégory Rogez, Philip Torr

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Causal Parrots: Large Language Models May Talk Causality but Are Not Causal Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting

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Causal Reinforcement Learning: A Survey Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang

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Causally-Guided Regularization of Graph Attention Improves Generalizability Alexander P Wu, Thomas Markovich, Bonnie Berger, Nils Yannick Hammerla, Rohit Singh

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Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

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Chasing Better Deep Image Priors Between Over- and Under-Parameterization Qiming Wu, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

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Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

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ChemSpacE: Interpretable and Interactive Chemical Space Exploration Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, Bolei Zhou

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Clustering Using Approximate Nearest Neighbour Oracles Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman

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CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization Kilian Pfeiffer, Martin Rapp, Ramin Khalili, Joerg Henkel

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Communication-Efficient Distributionally Robust Decentralized Learning Matteo Zecchin, Marios Kountouris, David Gesbert

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Comparative Generalization Bounds for Deep Neural Networks Tomer Galanti, Liane Galanti, Ido Ben-Shaul

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Complementary Sparsity: Accelerating Sparse CNNs with High Accuracy on General-Purpose Computing Platforms Kang Zhao, Yijun Tan, Kai Han, Ting Hu, Hanting Chen, Tao Yuan, Yunhe Wang, Jun Yao

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Computationally-Efficient Initialisation of GPs: The Generalised Variogram Method Felipe Tobar, Elsa Cazelles, Taco de Wolff

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Conditional Generative Models Are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems Fabian Altekrüger, Paul Hagemann, Gabriele Steidl

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Conditional Permutation Invariant Flows Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood

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Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling Vaidotas Simkus, Michael U. Gutmann

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Conformal Prediction Under Ambiguous Ground Truth David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, Arnaud Doucet

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Consistent Collaborative Filtering via Tensor Decomposition Shiwen Zhao, Guillermo Sapiro

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Constrained Parameter Inference as a Principle for Learning Nasir Ahmad, Ellen Schrader, Marcel van Gerven

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Containing a Spread Through Sequential Learning: To Exploit or to Explore? Xingran Chen, Hesam Nikpey, Jungyeol Kim, Saswati Sarkar, Shirin Saeedi Bidokhti

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Contextual Combinatorial Multi-Output GP Bandits with Group Constraints Sepehr Elahi, Baran Atalar, Sevda Öğüt, Cem Tekin

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Contextualize Me – The Case for Context in Reinforcement Learning Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer

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Continual Learning by Modeling Intra-Class Variation Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu

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Contrastive Attraction and Contrastive Repulsion for Representation Learning Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya Zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

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Contrastive Search Is What You Need for Neural Text Generation Yixuan Su, Nigel Collier

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Controlling Neural Network Smoothness for Neural Algorithmic Reasoning David A. Klindt

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Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses Eloi Tanguy

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Costs and Benefits of Fair Regression Han Zhao

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Cox-Hawkes: Doubly Stochastic Spatiotemporal Poisson Processes Xenia Miscouridou, Samir Bhatt, George Mohler, Seth Flaxman, Swapnil Mishra

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Cross-Client Label Propagation for Transductive and Semi-Supervised Federated Learning Jonathan Scott, Michelle Yeo, Christoph H Lampert

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Cross-Validation for Geospatial Data: Estimating Generalization Performance in Geostatistical Problems Jing Wang, Laurel Hopkins, Tyler Hallman, W. Douglas Robinson, Rebecca Hutchinson

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Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize Mert Gurbuzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu

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Cyclophobic Reinforcement Learning Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling

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Data Augmentation Is a Hyperparameter: Cherry-Picked Self-Supervision for Unsupervised Anomaly Detection Is Creating the Illusion of Success Jaemin Yoo, Tiancheng Zhao, Leman Akoglu

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Data Distillation: A Survey Noveen Sachdeva, Julian McAuley

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Data Models for Dataset Drift Controls in Machine Learning with Optical Images Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti

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Data Pruning and Neural Scaling Laws: Fundamental Limitations of Score-Based Algorithms Fadhel Ayed, Soufiane Hayou

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Data-Free Diversity-Based Ensemble Selection for One-Shot Federated Learning Naibo Wang, Wenjie Feng, Yuchen Deng, Moming Duan, Fusheng Liu, See-Kiong Ng

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Deep Double Descent via Smooth Interpolation Matteo Gamba, Erik Englesson, Mårten Björkman, Hossein Azizpour

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Deep Operator Learning Lessens the Curse of Dimensionality for PDEs Ke Chen, Chunmei Wang, Haizhao Yang

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Deep Plug-and-Play Clustering with Unknown Number of Clusters An Xiao, Hanting Chen, Tianyu Guo, Qinghua Zhang, Yunhe Wang

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Defense Against Reward Poisoning Attacks in Reinforcement Learning Kiarash Banihashem, Adish Singla, Goran Radanovic

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Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, Josef Teichmann

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Detecting Danger in Gridworlds Using Gromov’s Link Condition Thomas F Burns, Robert Tang

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Detecting Incidental Correlation in Multimodal Learning via Latent Variable Modeling Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

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DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor I Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

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Diagnostic Tool for Out-of-Sample Model Evaluation Ludvig Hult, Dave Zachariah, Peter Stoica

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Differentiable Logic Machines Matthieu Zimmer, Xuening Feng, Claire Glanois, Zhaohui Jiang, Jianyi Zhang, Paul Weng, Dong Li, Jianye Hao, Wulong Liu

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Differentially Private Diffusion Models Tim Dockhorn, Tianshi Cao, Arash Vahdat, Karsten Kreis

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Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with Log-Euclidean Metric Saiteja Utpala, Praneeth Vepakomma, Nina Miolane

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Differentially Private Image Classification from Features Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

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Differentially Private Optimizers Can Learn Adversarially Robust Models Zhiqi Bu, Yuan Zhang

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Differentially Private Partitioned Variational Inference Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela

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Diffusion Models for Constrained Domains Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

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Diffusion-Based Time Series Imputation and Forecasting with Structured State Space Models Juan Lopez Alcaraz, Nils Strodthoff

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Dirichlet Mechanism for Differentially Private KL Divergence Minimization Donlapark Ponnoprat

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DisCo: Improving Compositional Generalization in Visual Reasoning Through Distribution Coverage Joy Hsu, Jiayuan Mao, Jiajun Wu

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Discretization Invariant Networks for Learning Maps Between Neural Fields Clinton Wang, Polina Golland

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Distributed Architecture Search over Heterogeneous Distributions Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr

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Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov, Xun Qian, Slavomir Hanzely, Mher Safaryan, Peter Richtárik

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Distributionally Robust Classification on a Data Budget Benjamin Feuer, Ameya Joshi, Minh Pham, Chinmay Hegde

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Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? Tian Yun, Usha Bhalla, Ellie Pavlick, Chen Sun

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DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity Chung-Yiu Yau, Hoi To Wai

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Does ‘Deep Learning on a Data Diet’ Reproduce? Overall Yes, but GraNd at Initialization Does Not Andreas Kirsch

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DORA: Exploring Outlier Representations in Deep Neural Networks Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus Robert Muller, Marina MC Höhne

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DP-LFlow: Differentially Private Latent Flow for Scalable Sensitive Image Generation Dihong Jiang, Sun Sun

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DPVIm: Differentially Private Variational Inference Improved Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski

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Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data Yuji Roh, Weili Nie, De-An Huang, Steven Euijong Whang, Arash Vahdat, Anima Anandkumar

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DreamEdit: Subject-Driven Image Editing Tianle Li, Max Ku, Cong Wei, Wenhu Chen

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Dropped Scheduled Task: Mitigating Negative Transfer in Multi-Task Learning Using Dynamic Task Dropping Aakarsh Malhotra, Mayank Vatsa, Richa Singh

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DSpar: An Embarrassingly Simple Strategy for Efficient GNN Training and Inference via Degree-Based Sparsification Zirui Liu, Kaixiong Zhou, Zhimeng Jiang, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

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Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning Shruthi Gowda, Bahram Zonooz, Elahe Arani

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Dual PatchNorm Manoj Kumar, Mostafa Dehghani, Neil Houlsby

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Dual Representation Learning for Out-of-Distribution Detection Zhilin Zhao, Longbing Cao

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Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-Convex Problems Ting-Jui Chang, Sapana Chaudhary, Dileep Kalathil, Shahin Shahrampour

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Dynamic Subgoal-Based Exploration via Bayesian Optimization Yijia Wang, Matthias Poloczek, Daniel R. Jiang

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Dynamics Adapted Imitation Learning Zixuan Liu, Liu Liu, Bingzhe Wu, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao

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Early Stopping for Deep Image Prior Hengkang Wang, Taihui Li, Zhong Zhuang, Tiancong Chen, Hengyue Liang, Ju Sun

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ECG Representation Learning with Multi-Modal EHR Data Sravan Kumar Lalam, Hari Krishna Kunderu, Shayan Ghosh, Harish Kumar A, Samir Awasthi, Ashim Prasad, Francisco Lopez-Jimenez, Zachi I Attia, Samuel Asirvatham, Paul Friedman, Rakesh Barve, Melwin Babu

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EdiBERT: A Generative Model for Image Editing Thibaut Issenhuth, Ugo Tanielian, Jeremie Mary, David Picard

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Efficient Inference with Model Cascades Luzian Lebovitz, Lukas Cavigelli, Michele Magno, Lorenz K Muller

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Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning Barna Pásztor, Andreas Krause, Ilija Bogunovic

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Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning Yinglun Xu, Qi Zeng, Gagandeep Singh

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Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning Nikhil Vyas, Yamini Bansal, Preetum Nakkiran

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Empirical Study on Optimizer Selection for Out-of-Distribution Generalization Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas

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Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance Bahjat Kawar, Roy Ganz, Michael Elad

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Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy

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Equivariant MuZero Andreea Deac, Theophane Weber, George Papamakarios

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Error Bounds and Dynamics of Bootstrapping in Actor-Critic Reinforcement Learning Ahmed J Zerouali, Douglas Blair Tweed

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Estimating Differential Equations from Temporal Point Processes Shuichi Miyazawa, Daichi Mochihashi

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Estimating the Density Ratio Between Distributions with High Discrepancy Using Multinomial Logistic Regression Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann

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Euclidean-Norm-Induced Schatten-P Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell

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Evaluating Human-Language Model Interaction Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael S. Bernstein, Percy Liang

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Event Tables for Efficient Experience Replay Varun Raj Kompella, Thomas Walsh, Samuel Barrett, Peter R. Wurman, Peter Stone

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Execution-Based Code Generation Using Deep Reinforcement Learning Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy

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Expected Worst Case Regret via Stochastic Sequential Covering Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski

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Explaining Visual Counterfactual Explainers Diego Velazquez, Pau Rodriguez, Alexandre Lacoste, Issam H. Laradji, Xavier Roca, Jordi Gonzàlez

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Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions Ido Ben-Shaul, Tomer Galanti, Shai Dekel

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Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation YiFan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric Xing

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Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis Jing Wu, David Pichler, Daniel Marley, Naira Hovakimyan, David A Wilson, Jennifer Hobbs

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Extreme Masking for Learning Instance and Distributed Visual Representations Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin

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Fair and Useful Cohort Selection Konstantina Bairaktari, Paul Tsela Langton, Huy Nguyen, Niklas Smedemark-Margulies, Jonathan Ullman

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Fair Kernel Regression Through Cross-Covariance Operators Adrian Perez-Suay, Paula Gordaliza, Jean-Michel Loubes, Dino Sejdinovic, Gustau Camps-Valls

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FairGrad: Fairness Aware Gradient Descent Gaurav Maheshwari, Michaël Perrot

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Fairness and Robustness in Anti-Causal Prediction Maggie Makar, Alexander D'Amour

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Fairness via In-Processing in the Over-Parameterized Regime: A Cautionary Tale with MinDiff Loss Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg

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Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc

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Fast Slate Policy Optimization: Going Beyond Plackett-Luce Otmane Sakhi, David Rohde, Nicolas Chopin

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Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration Newton Mwai Kinyanjui, Emil Carlsson, Fredrik D. Johansson

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Fast&Fair: Training Acceleration and Bias Mitigation for GNNs Oyku Deniz Kose, Yanning Shen

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Faster Training of Neural ODEs Using Gauß–Legendre Quadrature Alexander Luke Ian Norcliffe, Marc Peter Deisenroth

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FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks Amrit Nagarajan, Anand Raghunathan

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Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning Wilka Torrico Carvalho, Andrew Kyle Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan

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FedDAG: Federated DAG Structure Learning Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell

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Federated High-Dimensional Online Decision Making Chi-Hua Wang, Wenjie Li, Guang Lin

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Federated Learning Under Covariate Shifts with Generalization Guarantees Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher

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Federated Learning Under Partially Disjoint Data via Manifold Reshaping Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu, Yanfeng Wang, Ya Zhang

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Federated Minimax Optimization with Client Heterogeneity Pranay Sharma, Rohan Panda, Gauri Joshi

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Finding and Only Finding Differential Nash Equilibria by Both Pretending to Be a Follower Xuchan Bao, Guodong Zhang

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Finding Competence Regions in Domain Generalization Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Koethe

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Finding Neurons in a Haystack: Case Studies with Sparse Probing Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, Dimitris Bertsimas

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Finite-Time Analysis of Decentralized Single-Timescale Actor-Critic Qijun Luo, Xiao Li

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FLUID: A Unified Evaluation Framework for Flexible Sequential Data Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi

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Foiling Explanations in Deep Neural Networks Snir Vitrack Tamam, Raz Lapid, Moshe Sipper

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Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi Jaakkola

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Fourier Features in Reinforcement Learning with Neural Networks David Brellmann, David Filliat, Goran Frehse

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FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction Alexander Telepov, Artem Tsypin, Kuzma Khrabrov, Sergey Yakukhnov, Pavel Strashnov, Petr Zhilyaev, Egor Rumiantsev, Daniel Ezhov, Manvel Avetisian, Olga Popova, Artur Kadurin

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Fusion of Global and Local Knowledge for Personalized Federated Learning Tiansheng Huang, Li Shen, Yan Sun, Weiwei Lin, Dacheng Tao

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Gated Domain Units for Multi-Source Domain Generalization Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thaeter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet

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Generalizability of Adversarial Robustness Under Distribution Shifts Kumail Alhamoud, Hasan Abed Al Kader Hammoud, Motasem Alfarra, Bernard Ghanem

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Generalization as Dynamical Robustness--the Role of Riemannian Contraction in Supervised Learning Leo Kozachkov, Patrick Wensing, Jean-Jacques Slotine

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Generalization Bounds for Kernel Canonical Correlation Analysis Enayat Ullah, Raman Arora

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Generating Adversarial Examples with Task Oriented Multi-Objective Optimization Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Phung

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Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity Arrasy Rahman, Elliot Fosong, Ignacio Carlucho, Stefano V Albrecht

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GIT-Net: Generalized Integral Transform for Operator Learning Chao Wang, Alexandre H. Thiery

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Global Contrastive Learning for Long-Tailed Classification Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, Thanh Nguyen-Tang

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GPS++: Reviving the Art of Message Passing for Molecular Property Prediction Dominic Masters, Josef Dean, Kerstin Klaeser, Zhiyi Li, Samuel Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew W Fitzgibbon, Shenyang Huang, Ladislav Rampášek, Dominique Beaini

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Gradient Masked Averaging for Federated Learning Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky

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Gradient-Adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven

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Graph Neural Networks Designed for Different Graph Types: A Survey Josephine Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara Holzhüter

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Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini

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Graph-Based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting Zibo Liu, Parshin Shojaee, Chandan K. Reddy

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GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao

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Greedier Is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering Jwo-Yuh Wu, Liang-Chi Huang, Wen Hsuan Li, Chun-Hung Liu, Rung-Hung Gau

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Group Fairness in Reinforcement Learning Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau

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GSR: A Generalized Symbolic Regression Approach Tony Tohme, Dehong Liu, Kamal Youcef-Toumi

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Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J Foster, Lekan P Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

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Guillotine Regularization: Why Removing Layers Is Needed to Improve Generalization in Self-Supervised Learning Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent

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HERMES: Hybrid Error-Corrector Model with Inclusion of External Signals for Nonstationary Fashion Time Series Etienne David, Jean Bellot, Sylvain Le Corff

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Hidden Heterogeneity: When to Choose Similarity-Based Calibration Kiri L. Wagstaff, Thomas G Dietterich

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High Fidelity Neural Audio Compression Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi

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High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Russ Salakhutdinov

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Holistic Evaluation of Language Models Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D Manning, Christopher Re, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda

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Homomorphic Self-Supervised Learning T. Anderson Keller, Xavier Suau, Luca Zappella

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How Reliable Is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

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How Robust Is Your Fairness? Evaluating and Sustaining Fairness Under Unseen Distribution Shifts Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang

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How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition Jorge A Mendez, Eric Eaton

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HypUC: Hyperfine Uncertainty Calibration with Gradient- Boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms Uddeshya Upadhyay, Sairam Bade, Arjun Puranik, Shahir Asfahan, Melwin Babu, Francisco Lopez-Jimenez, Samuel Asirvatham, Ashim Prasad, Ajit Rajasekharan, Samir Awasthi, Rakesh Barve

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IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function Shivani Bathla, Vinita Vasudevan

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Identification of Negative Transfers in Multitask Learning Using Surrogate Models Dongyue Li, Huy Nguyen, Hongyang Ryan Zhang

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Identifying Latent Distances with Finslerian Geometry Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg

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ILPO-MP: Mode Priors Prevent Mode Collapse When Imitating Latent Policies from Observations Oliver Struckmeier, Ville Kyrki

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Image Compression with Product Quantized Masked Image Modeling Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, Herve Jegou

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Image Retrieval Outperforms Diffusion Models on Data Augmentation Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell

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Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning Macheng Shen, Jonathan P How

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Improved Baselines for Vision-Language Pre-Training Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano, Jakob Verbeek, Michal Drozdzal

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Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra

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Improved Group Robustness via Classifier Retraining on Independent Splits Thien Hang Nguyen, Hongyang R. Zhang, Huy Nguyen

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Improved Identification Accuracy in Equation Learning via Comprehensive $\boldsymbol{R^2}$-Elimination and Bayesian Model Selection Daniel Nickelsen, Bubacarr Bah

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Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks Bartłomiej Polaczyk, Jacek Cyranka

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Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

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Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu, Matthew B. Blaschko

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Improving Generalization with Approximate Factored Value Functions Shagun Sodhani, Sergey Levine, Amy Zhang

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Improving Native CNN Robustness with Filter Frequency Regularization Jovita Lukasik, Paul Gavrikov, Janis Keuper, Margret Keuper

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In Search of Projectively Equivariant Networks Georg Bökman, Axel Flinth, Fredrik Kahl

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IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for All 22 Scheduled Indian Languages Jay Gala, Pranjal A Chitale, A K Raghavan, Varun Gumma, Sumanth Doddapaneni, Aswanth Kumar M, Janki Atul Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M Khapra, Raj Dabre, Anoop Kunchukuttan

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Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang

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Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention Melika Behjati, James Henderson

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Inherent Limits on Topology-Based Link Prediction Justus Isaiah Hibshman, Tim Weninger

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Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke J. Wiggers, Taco Cohen

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Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders Jacobie Mouton, Rodney Stephen Kroon

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Interpretable Mixture of Experts Aya Abdelsalam Ismail, Sercan O Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister

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Intrinsic Dimension for Large-Scale Geometric Learning Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider

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Invariant Feature Coding Using Tensor Product Representation Yusuke Mukuta, Tatsuya Harada

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Invariant Structure Learning for Better Generalization and Causal Explainability Yunhao Ge, Sercan O Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister

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Inverse Scaling: When Bigger Isn't Better Ian R. McKenzie, Alexander Lyzhov, Michael Martin Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Xudong Shen, Joe Cavanagh, Andrew George Gritsevskiy, Derik Kauffman, Aaron T. Kirtland, Zhengping Zhou, Yuhui Zhang, Sicong Huang, Daniel Wurgaft, Max Weiss, Alexis Ross, Gabriel Recchia, Alisa Liu, Jiacheng Liu, Tom Tseng, Tomasz Korbak, Najoung Kim, Samuel R. Bowman, Ethan Perez

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Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration Mauricio Delbracio, Peyman Milanfar

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Invertible Hierarchical Generative Model for Images Heikki Timonen, Miika Aittala, Jaakko Lehtinen

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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White

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Jacobian-Based Causal Discovery with Nonlinear ICA Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel

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JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games Yang Li, Kun Xiong, Yingping Zhang, Jiangcheng Zhu, Stephen Marcus McAleer, Wei Pan, Jun Wang, Zonghong Dai, Yaodong Yang

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Know Your Self-Supervised Learning: A Survey on Image-Based Generative and Discriminative Training Utku Ozbulak, Hyun Jung Lee, Beril Boga, Esla Timothy Anzaku, Ho-min Park, Arnout Van Messem, Wesley De Neve, Joris Vankerschaver

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KRADA: Known-Region-Aware Domain Alignment for Open-Set Domain Adaptation in Semantic Segmentation Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, William Cheung, Bo Han

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L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao, Boxiang Lyu, Mladen Kolar

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Label Noise-Robust Learning Using a Confidence-Based Sieving Strategy Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis

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Latent State Models of Training Dynamics Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho

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Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K Warmuth

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LEAD: Min-Max Optimization from a Physical Perspective Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

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Learn the Time to Learn: Replay Scheduling in Continual Learning Marcus Klasson, Hedvig Kjellstrom, Cheng Zhang

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Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks Vijaya Raghavan T Ramkumar, Elahe Arani, Bahram Zonooz

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Learned Thresholds Token Merging and Pruning for Vision Transformers Maxim Bonnaerens, Joni Dambre

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Learning Augmentation Distributions Using Transformed Risk Minimization Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis, Edgar Dobriban

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Learning Domain-Specific Causal Discovery from Time Series Xinyue Wang, Konrad Kording

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Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes Magnus Ross, Markus Heinonen

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Learning from Time-Dependent Streaming Data with Online Stochastic Algorithms Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger

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Learning Graph Structure from Convolutional Mixtures Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro

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Learning Identity-Preserving Transformations on Data Manifolds Marissa Catherine Connor, Kion Fallah, Christopher John Rozell

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Learning Interpolations Between Boltzmann Densities Bálint Máté, François Fleuret

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Learning Multiscale Non-Stationary Causal Structures Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi, Francesco Bonchi

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Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition Hong-Xing Yu, Michelle Guo, Alireza Fathi, Yen-Yu Chang, Eric Ryan Chan, Ruohan Gao, Thomas Funkhouser, Jiajun Wu

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Learning Representations for Pixel-Based Control: What Matters and Why? Manan Tomar, Utkarsh Aashu Mishra, Amy Zhang, Matthew E. Taylor

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Learning Representations That Are Closed-Form Monge Mapping Optimal with Application to Domain Adaptation Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki

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Learning Symbolic Rules for Reasoning in Quasi-Natural Language Kaiyu Yang, Jia Deng

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Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring Shanchao Yang, Ma Kaili, Baoxiang Wang, Tianshu Yu, Hongyuan Zha

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Learning to Correct Spectral Methods for Simulating Turbulent Flows Gideon Dresdner, Dmitrii Kochkov, Peter Christian Norgaard, Leonardo Zepeda-Nunez, Jamie Smith, Michael Brenner, Stephan Hoyer

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Learning to Incentivize Improvements from Strategic Agents Yatong Chen, Jialu Wang, Yang Liu

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Learning to Look by Self-Prediction Matthew Koichi Grimes, Joseph Varughese Modayil, Piotr W Mirowski, Dushyant Rao, Raia Hadsell

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Learning to Optimize Quasi-Newton Methods Isaac Liao, Rumen Dangovski, Jakob Nicolaus Foerster, Marin Soljacic

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Learning to Reconstruct Signals from Binary Measurements Alone Julián Tachella, Laurent Jacques

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Learning-to-Defer for Sequential Medical Decision-Making Under Uncertainty Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

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Leveraging Demonstrations with Latent Space Priors Jonas Gehring, Deepak Gopinath, Jungdam Won, Andreas Krause, Gabriel Synnaeve, Nicolas Usunier

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Lifelong Reinforcement Learning with Modulating Masks Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen Kumar Pilly, Soheil Kolouri, Andrea Soltoggio

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Lightweight Learner for Shared Knowledge Lifelong Learning Yunhao Ge, Yuecheng Li, Di Wu, Ao Xu, Adam M. Jones, Amanda Sofie Rios, Iordanis Fostiropoulos, Shixian Wen, Po-Hsuan Huang, Zachary William Murdock, Gozde Sahin, Shuo Ni, Kiran Lekkala, Sumedh Anand Sontakke, Laurent Itti

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Limitation of Characterizing Implicit Regularization by Data-Independent Functions Leyang Zhang, Zhi-Qin John Xu, Tao Luo, Yaoyu Zhang

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Linearized Relative Positional Encoding Zhen Qin, Weixuan Sun, Kaiyue Lu, Hui Deng, Dongxu Li, Xiaodong Han, Yuchao Dai, Lingpeng Kong, Yiran Zhong

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Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks Jarrod Haas, William Yolland, Bernhard T Rabus

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Lo-Fi: Distributed Fine-Tuning Without Communication Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael Rabbat, Ari S. Morcos

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Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs Raphaël Avalos, Mathieu Reymond, Ann Nowe, Diederik M Roijers

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Local Function Complexity for Active Learning via Mixture of Gaussian Processes Danny Panknin, Stefan Chmiela, Klaus Robert Muller, Shinichi Nakajima

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Logistic-Normal Likelihoods for Heteroscedastic Label Noise Erik Englesson, Amir Mehrpanah, Hossein Azizpour

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Long-Term Forecasting with TiDE: Time-Series Dense Encoder Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan K Mathur, Rajat Sen, Rose Yu

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Machine Explanations and Human Understanding Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan

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MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks Weicheng Kuo, Aj Piergiovanni, Dahun Kim, Xiyang Luo, Benjamin Caine, Wei Li, Abhijit Ogale, Luowei Zhou, Andrew M. Dai, Zhifeng Chen, Claire Cui, Anelia Angelova

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MASIF: Meta-Learned Algorithm Selection Using Implicit Fidelity Information Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer

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Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli

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Mean-Field Control Based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-Decomposable Shared Global State Washim Uddin Mondal, Vaneet Aggarwal, Satish Ukkusuri

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Memory-Efficient Reinforcement Learning with Value-Based Knowledge Consolidation Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood

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MERMAIDE: Learning to Align Learners Using Model-Based Meta-Learning Arundhati Banerjee, Soham Rajesh Phade, Stefano Ermon, Stephan Zheng

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Meta Continual Learning on Graphs with Experience Replay Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal

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Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error Ondrej Bohdal, Yongxin Yang, Timothy Hospedales

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Meta-Learning via Classifier(-Free) Diffusion Guidance Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F Grewe

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Mind the Gap: Mitigating the Distribution Gap in Graph Few-Shot Learning Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye, Chuxu Zhang

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Minorization-Maximization for Learning Determinantal Point Processes Takahiro Kawashima, Hideitsu Hino

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Mitigating Confirmation Bias in Semi-Supervised Learning via Efficient Bayesian Model Averaging Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

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Mitigating Real-World Distribution Shifts in the Fourier Domain Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo

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Mixed Effects in Machine Learning – A Flexible mixedML Framework to Add Random Effects to Supervised Machine Learning Regression Pascal Kilian, Sangbeak Ye, Augustin Kelava

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Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation Xiaoyu Lin, Laurent Girin, Xavier Alameda-Pineda

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mL-BFGS: A Momentum-Based L-BFGS for Distributed Large-Scale Neural Network Optimization Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr

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Modelling Sequential Branching Dynamics with a Multivariate Branching Gaussian Process Elvijs Sarkans, Sumon Ahmed, Magnus Rattray, Alexis Boukouvalas

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Modular Deep Learning Jonas Pfeiffer, Sebastian Ruder, Ivan Vulić, Edoardo Ponti

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Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada

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Monotone Deep Boltzmann Machines Zhili Feng, Ezra Winston, J Zico Kolter

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Multi-Annotator Deep Learning: A Probabilistic Framework for Classification Marek Herde, Denis Huseljic, Bernhard Sick

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Multi-Dimensional Concept Discovery (MCD): A Unifying Framework with Completeness Guarantees Johanna Vielhaben, Stefan Bluecher, Nils Strodthoff

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Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations Xinyu Yang, Huaxiu Yao, Allan Zhou, Chelsea Finn

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Multi-Label Node Classification on Graph-Structured Data Tianqi Zhao, Thi Ngan Dong, Alan Hanjalic, Megha Khosla

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Multi-Objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows Alina Selega, Kieran R. Campbell

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Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Remo Sasso, Matthia Sabatelli, Marco A. Wiering

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Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities Kasra Darvish, Edward Raff, Francis Ferraro, Cynthia Matuszek

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Multiscale Causal Structure Learning Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa

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Named Tensor Notation David Chiang, Alexander M Rush, Boaz Barak

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Neighborhood Gradient Mean: An Efficient Decentralized Learning Method for Non-IID Data Sai Aparna Aketi, Sangamesh Kodge, Kaushik Roy

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Neural Causal Structure Discovery from Interventions Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael Curtis Mozer, Christopher Pal, Yoshua Bengio

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Neural Collapse: A Review on Modelling Principles and Generalization Vignesh Kothapalli

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Neural Monge mAP Estimation and Its Applications Jiaojiao Fan, Shu Liu, Shaojun Ma, Hao-Min Zhou, Yongxin Chen

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Neural Networks Beyond Explainability: Selective Inference for Sequence Motifs Antoine Villié, Philippe Veber, Yohann De Castro, Laurent Jacob

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Neural Ordinary Differential Equations for Modeling Epidemic Spreading Chrysoula Kosma, Giannis Nikolentzos, George Panagopoulos, Jean-Marc Steyaert, Michalis Vazirgiannis

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Neural Shape Compiler: A Unified Framework for Transforming Between Text, Point Cloud, and Program Tiange Luo, Honglak Lee, Justin Johnson

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NOFLITE: Learning to Predict Individual Treatment Effect Distributions Toon Vanderschueren, Jeroen Berrevoets, Wouter Verbeke

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Noise-Robust Graph Learning by Estimating and Leveraging Pairwise Interactions Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang

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Non-Stationary Contextual Pricing with Safety Constraints Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang

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Nonconvex-Nonconcave Min-Max Optimization on Riemannian Manifolds Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

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Not All Causal Inference Is the Same Matej Zečević, Devendra Singh Dhami, Kristian Kersting

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Novel Class Discovery for Long-Tailed Recognition Chuyu Zhang, Ruijie Xu, Xuming He

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NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds Cynthia Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C Hughes

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Numerical Accounting in the Shuffle Model of Differential Privacy Antti Koskela, Mikko A. Heikkilä, Antti Honkela

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Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach Florian Lalande, Kenji Doya

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OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing Firat Ozdemir, Berkan Lafci, Xose Luis Dean-Ben, Daniel Razansky, Fernando Perez-Cruz

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Off-Policy Evaluation with Out-of-Sample Guarantees Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica

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Offline Reinforcement Learning with Additional Covering Distributions Chenjie Mao

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Offline Reinforcement Learning with Mixture of Deterministic Policies Takayuki Osa, Akinobu Hayashi, Pranav Deo, Naoki Morihira, Takahide Yoshiike

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On a Continuous Time Model of Gradient Descent Dynamics and Instability in Deep Learning Mihaela Rosca, Yan Wu, Chongli Qin, Benoit Dherin

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On Adaptivity in Quantum Testing Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir

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On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature Xu Cai, Thanh Lam, Jonathan Scarlett

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On Averaging ROC Curves Jack Hogan, Niall M. Adams

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On Perfect Clustering for Gaussian Processes Juan Cuesta-Albertos, Subhajit Dutta

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On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long

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On the Efficacy of Differentially Private Few-Shot Image Classification Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

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On the Gradient Formula for Learning Generative Models with Regularized Optimal Transport Costs Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin

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On the Infinite-Depth Limit of Finite-Width Neural Networks Soufiane Hayou

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On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-Time Event Data Tanguy Bosser, Souhaib Ben Taieb

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On the Robustness of Dataset Inference Sebastian Szyller, Rui Zhang, Jian Liu, N Asokan

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On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C Geiger

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On the Sample Complexity of Lipschitz Constant Estimation Julien Walden Huang, Stephen J. Roberts, Jan-Peter Calliess

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On the Special Role of Class-Selective Neurons in Early Training Omkar Ranadive, Nikhil Thakurdesai, Ari S. Morcos, Matthew L Leavitt, Stephane Deny

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On the Statistical Complexity of Estimation and Testing Under Privacy Constraints Clément Lalanne, Aurélien Garivier, Rémi Gribonval

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One-Round Active Learning Through Data Utility Learning and Proxy Models Jiachen T. Wang, Si Chen, Ruoxi Jia

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One-Step Distributional Reinforcement Learning Mastane Achab, Reda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines

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Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness Muhammad Osama, Dave Zachariah, Peter Stoica, Thomas B. Schön

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Online Min-Max Problems with Non-Convexity and Non-Stationarity Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang

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Online Model Selection by Learning How Compositional Kernels Evolve Eura Shin, Predrag Klasnja, Susan Murphy, Finale Doshi-Velez

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Online Optimal Tracking of Linear Systems with Adversarial Disturbances Farnaz Adib Yaghmaie, Hamidreza Modares

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Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomek Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphael Segerie, Micah Carroll, Andi Peng, Phillip J.K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell

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OpenCon: Open-World Contrastive Learning Yiyou Sun, Yixuan Li

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Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions Zhiying Fang, Guang Cheng

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Optimal Threshold Labeling for Ordinal Regression Methods Ryoya Yamasaki

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Optimistic Optimization of Gaussian Process Samples Julia Grosse, Cheng Zhang, Philipp Hennig

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Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks Shiyu Liu, Rohan Ghosh, John Chong Min Tan, Mehul Motani

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Optimum-Statistical Collaboration Towards General and Efficient Black-Box Optimization Wenjie Li, Chi-Hua Wang, Guang Cheng, Qifan Song

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Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with Only Weak Clients Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr

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PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses Through Supermartingales Maxime Haddouche, Benjamin Guedj

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Pairwise Learning with Adaptive Online Gradient Descent Tao Sun, Qingsong Wang, Yunwen Lei, Dongsheng Li, Bao Wang

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Parameter Efficient Node Classification on Homophilic Graphs Lucas Prieto, Jeroen Den Boef, Paul Groth, Joran Cornelisse

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Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning Filippos Christianos, Georgios Papoudakis, Stefano V Albrecht

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Pareto Optimization for Active Learning Under Out-of-Distribution Data Scenarios Xueying Zhan, Zeyu Dai, Qingzhong Wang, Qing Li, Haoyi Xiong, Dejing Dou, Antoni B. Chan

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Partial Optimal Transport for Support Subset Selection Bilal Riaz, Yuksel Karahan, Austin J. Brockmeier

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Partition-Based Active Learning for Graph Neural Networks Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei

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Patches Are All You Need? Asher Trockman, J Zico Kolter

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PAVI: Plate-Amortized Variational Inference Louis Rouillard, Alexandre Le Bris, Thomas Moreau, Demian Wassermann

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PCPs: Patient Cardiac Prototypes to Probe AI-Based Medical Diagnoses, Distill Datasets, and Retrieve Patients Dani Kiyasseh, Tingting Zhu, David A. Clifton

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Personalized Federated Learning with Communication Compression El houcine Bergou, Konstantin Pavlovich Burlachenko, Aritra Dutta, Peter Richtárik

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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques Filip Hanzely, Boxin Zhao, Mladen Kolar

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Physics Informed Neural Networks for Elliptic Equations with Oscillatory Differential Operators Arnav Gangal, Luis Kim, Sean Patrick Carney

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Policy Gradient Algorithms Implicitly Optimize by Continuation Adrien Bolland, Gilles Louppe, Damien Ernst

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POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer

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PolyViT: Co-Training Vision Transformers on Images, Videos and Audio Valerii Likhosherstov, Anurag Arnab, Krzysztof Marcin Choromanski, Mario Lucic, Yi Tay, Mostafa Dehghani

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POMRL: No-Regret Learning-to-Plan with Increasing Horizons Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy

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Population-Based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Perolat

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Positive Difference Distribution for Image Outlier Detection Using Normalizing Flows and Contrastive Data Robert Schmier, Ullrich Koethe, Christoph-Nikolas Straehle

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Pre-Trained Perceptual Features Improve Differentially Private Image Generation Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park

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Predicting Out-of-Domain Generalization with Neighborhood Invariance Nathan Hoyen Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi

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Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods for Uncertainty Estimation Dennis Thomas Ulmer, Christian Hardmeier, Jes Frellsen

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Privacy Budget Tailoring in Private Data Analysis Daniel Alabi, Chris Wiggins

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Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection Robert E. Tillman, Tucker Balch, Manuela Veloso

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Private GANs, Revisited Alex Bie, Gautam Kamath, Guojun Zhang

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Private Multi-Task Learning: Formulation and Applications to Federated Learning Shengyuan Hu, Steven Wu, Virginia Smith

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Probing Predictions on OOD Images via Nearest Categories Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri

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Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen

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Projected Randomized Smoothing for Certified Adversarial Robustness Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi

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Proportional Fairness in Federated Learning Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu

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ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method Miles Everett, Mingjun Zhong, Georgios Leontidis

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Provably Convergent Policy Optimization via Metric-Aware Trust Region Methods Jun Song, Niao He, Lijun Ding, Chaoyue Zhao

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Provably Personalized and Robust Federated Learning Mariel Werner, Lie He, Michael Jordan, Martin Jaggi, Sai Praneeth Karimireddy

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Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, Matthias Althoff

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Proximal Curriculum for Reinforcement Learning Agents Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban, Adish Singla

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PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets Shuo Sun, Molei Qin, Xinrun Wang, Bo An

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Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices Kartik Gupta, Marios Fournarakis, Matthias Reisser, Christos Louizos, Markus Nagel

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Quantum Policy Iteration via Amplitude Estimation and Grover Search – Towards Quantum Advantage for Reinforcement Learning Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl

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RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, KaShun Shum, Tong Zhang

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RCT Rejection Sampling for Causal Estimation Evaluation Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg, Rohit Bhattacharya

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RECLIP: Resource-Efficient CLIP by Training with Small Images Runze Li, Dahun Kim, Bir Bhanu, Weicheng Kuo

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Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs Mona Buisson-Fenet, Valery Morgenthaler, Sebastian Trimpe, Florent Di Meglio

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Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval Maurits Bleeker, Andrew Yates, Maarten de Rijke

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Regret Bounds for Satisficing in Multi-Armed Bandit Problems Thomas Michel, Hossein Hajiabolhassan, Ronald Ortner

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Regularized Training of Intermediate Layers for Generative Models for Inverse Problems Sean Gunn, Jorio Cocola, PAul HAnd

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Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward Washim Uddin Mondal, Vaneet Aggarwal

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Reinforcement Teaching Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo

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Relating Graph Auto-Encoders to Linear Models Solveig Klepper, Ulrike von Luxburg

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Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola Emmanuel Olatunji, Thorben Funke, Megha Khosla

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Reliable Active Learning via Influence Functions Meng Xia, Ricardo Henao

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Replay-Enhanced Continual Reinforcement Learning Tiantian Zhang, Kevin Zehua Shen, Zichuan Lin, Bo Yuan, Xueqian Wang, Xiu Li, Deheng Ye

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Representations and Computations in Transformers That Support Generalization on Structured Tasks Yuxuan Li, James McClelland

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Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning Erfan Miahi, Revan MacQueen, Alex Ayoub, Abbas Masoumzadeh, Martha White

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Retiring $\Delta \text{DP}$: New Distribution-Level Metrics for Demographic Parity Xiaotian Han, Zhimeng Jiang, Hongye Jin, Zirui Liu, Na Zou, Qifan Wang, Xia Hu

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Reusable Options Through Gradient-Based Meta Learning David Kuric, Herke van Hoof

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Revisiting Adversarial Training for the Worst-Performing Class Thomas Pethick, Grigorios Chrysos, Volkan Cevher

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Revisiting Hidden Representations in Transfer Learning for Medical Imaging Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina

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Revisiting Image Classifier Training for Improved Certified Robust Defense Against Adversarial Patches Aniruddha Saha, Shuhua Yu, Mohammad Sadegh Norouzzadeh, Wan-Yi Lin, Chaithanya Kumar Mummadi

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Revisiting Sparsity Hunting in Federated Learning: Why Does Sparsity Consensus Matter? Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, Salman Avestimehr

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Revisiting Topic-Guided Language Models Carolina Zheng, Keyon Vafa, David Blei

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Rewiring with Positional Encodings for Graph Neural Networks Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon

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RIFLE: Imputation and Robust Inference from Low Order Marginals Sina Baharlouei, Sze-Chuan Suen, Meisam Razaviyayn

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RIGNN: A Rationale Perspective for Semi-Supervised Open-World Graph Classification Xiao Luo, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Wei Ju, Ming Zhang, Yizhou Sun

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Risk Sensitive Dead-End Identification in Safety-Critical Offline Reinforcement Learning Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi

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RLTF: Reinforcement Learning from Unit Test Feedback Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Yang Wei, Deheng Ye

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Robust Alzheimer's Progression Modeling Using Cross-Domain Self-Supervised Deep Learning Saba Dadsetan, Mohsen Hejrati, Shandong Wu, Somaye Hashemifar

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Robust Hybrid Learning with Expert Augmentation Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen

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Robust Multi-Agent Reinforcement Learning with State Uncertainty Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, Fei Miao

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Robustness Through Data Augmentation Loss Consistency Tianjian Huang, Shaunak Ashish Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

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Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds Owen Melia, Eric M Jonas, Rebecca Willett

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SANTA: Source Anchoring Network and Target Alignment for Continual Test Time Adaptation Goirik Chakrabarty, Manogna Sreenivas, Soma Biswas

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SC2 Benchmark: Supervised Compression for Split Computing Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

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Scalable Deep Compressive Sensing Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu

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Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

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Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers Romain Menegaux, Emmanuel Jehanno, Margot Selosse, Julien Mairal

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Self-Supervised Graph Representation Learning for Neuronal Morphologies Marissa A. Weis, Laura Pede, Timo Lüddecke, Alexander S Ecker

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Self-Supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson’s Disease Fatemeh Haghighi, Soumitra Ghosh, Sarah Chu, Hai Ngu, Mohsen Hejrati, Han Hui Lin, Baris Bingol, Somaye Hashemifar

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Self-Supervision Is All You Need for Solving Rubik’s Cube Kyo Takano

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Semantic Representations of Mathematical Expressions in a Continuous Vector Space Neeraj Gangwar, Nickvash Kani

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Semantic Self-Adaptation: Enhancing Generalization with a Single Sample Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers, Stefan Roth

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Semi-Supervised Single Domain Generalization with Label-Free Adversarial Data Augmentation Ronghang Zhu, Xiang Yu, Sheng Li

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Separable Self-Attention for Mobile Vision Transformers Sachin Mehta, Mohammad Rastegari

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Sequential Query Encoding for Complex Query Answering on Knowledge Graphs Jiaxin Bai, Tianshi Zheng, Yangqiu Song

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SHAP-XRT: The Shapley Value Meets Conditional Independence Testing Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam

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Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling Alexander Tyurin, Lukang Sun, Konstantin Pavlovich Burlachenko, Peter Richtárik

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SIESTA: Efficient Online Continual Learning with Sleep Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Ronald Kemker, Christopher Kanan

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Signed Graph Neural Networks: A Frequency Perspective Rahul Singh, Yongxin Chen

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Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Multi-Scale Graph Networks Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, Tommi S. Jaakkola

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Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kenji Kawaguchi, Xiaokui Xiao

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SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin Riedmiller

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SMILE: Sample-to-Feature Mixup for Efficient Transfer Learning Xingjian Li, Haoyi Xiong, Cheng-zhong Xu, Dejing Dou

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Smoothed Differential Privacy Ao Liu, Yu-Xiang Wang, Lirong Xia

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Sobolev Spaces, Kernels and Discrepancies over Hyperspheres Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami

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Soft Diffusion: Score Matching with General Corruptions Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alex Dimakis, Peyman Milanfar

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SolidGen: An Autoregressive Model for Direct B-Rep Synthesis Pradeep Kumar Jayaraman, Joseph George Lambourne, Nishkrit Desai, Karl Willis, Aditya Sanghi, Nigel J. W. Morris

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Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm Yiling Xie, Yiling Luo, Xiaoming Huo

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Solving Nonconvex-Nonconcave Min-Max Problems Exhibiting Weak Minty Solutions Axel Böhm

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Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes Simon Buchholz

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SPADE: Semi-Supervised Anomaly Detection Under Distribution Mismatch Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Tomas Pfister

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Spectral Learning of Bernoulli Linear Dynamical Systems Models for Decision-Making Iris R Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow

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Spectral Regularization Allows Data-Frugal Learning over Combinatorial Spaces Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran

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Stacking Diverse Architectures to Improve Machine Translation Andrea Schioppa, Nal Kalchbrenner

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StarCoder: May the Source Be with You! Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Joel Lamy-Poirier, Joao Monteiro, Nicolas Gontier, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Ben Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason T Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Urvashi Bhattacharyya, Wenhao Yu, Sasha Luccioni, Paulo Villegas, Fedor Zhdanov, Tony Lee, Nadav Timor, Jennifer Ding, Claire S Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro Von Werra, Harm de Vries

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Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal

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Stochastic Constrained DRO with a Complexity Independent of Sample Size Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang

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Stochastic Gradient Updates Yield Deep Equilibrium Kernels Russell Tsuchida, Cheng Soon Ong

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Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas

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Straggler-Resilient Personalized Federated Learning Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari

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Structured Low-Rank Tensors for Generalized Linear Models Batoul Ahmad Taki, Anand Sarwate, Waheed U. Bajwa

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Subgraph Permutation Equivariant Networks Joshua Mitton, Roderick Murray-Smith

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Successor Feature Representations Chris Reinke, Xavier Alameda-Pineda

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Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu

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Supervised Knowledge May Hurt Novel Class Discovery Performance Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang

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Synthetic Data from Diffusion Models Improves ImageNet Classification Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet

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TabCBM: Concept-Based Interpretable Neural Networks for Tabular Data Mateo Espinosa Zarlenga, Zohreh Shams, Michael Edward Nelson, Been Kim, Mateja Jamnik

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Tackling Provably Hard Representative Selection via Graph Neural Networks Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, Mohammadhossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni

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Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity Christian Fröhlich, Robert Williamson

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Target Propagation via Regularized Inversion for Recurrent Neural Networks Vincent Roulet, Zaid Harchaoui

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Task Weighting in Meta-Learning with Trajectory Optimisation Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro

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Teaching Smaller Language Models to Generalise to Unseen Compositional Questions Tim Hartill, Neset Tan, Michael Witbrock, Patricia J. Riddle

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Temperature Check: Theory and Practice for Training Models with SoftMax-Cross-Entropy Losses Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann Dauphin

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Test-Time Adaptation for Visual Document Understanding Sayna Ebrahimi, Sercan O Arik, Tomas Pfister

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The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems Sumedh Pendurkar, Taoan Huang, Brendan Juba, Jiapeng Zhang, Sven Koenig, Guni Sharon

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The Analysis of the Expected Change in the Classification Probability of the Predicted Label Ruo Yang, Ping Liu, Mustafa Bilgic

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The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain Arsenii Kirillovich Moskvichev, Victor Vikram Odouard, Melanie Mitchell

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The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks James B Simon, Madeline Dickens, Dhruva Karkada, Michael R DeWeese

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The Geometry of Mixability Armando J Cabrera Pacheco, Robert Williamson

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The Kernel Density Integral Transformation Calvin McCarter

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The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola

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The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina MC Höhne

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The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning Ludvig Killingberg, Helge Langseth

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The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

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The Robustness Limits of SoTA Vision Models to Natural Variation Mark Ibrahim, Quentin Garrido, Ari S. Morcos, Diane Bouchacourt

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The Score-Difference Flow for Implicit Generative Modeling Romann M. Weber

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The Stack: 3 TB of Permissively Licensed Source Code Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Yacine Jernite, Margaret Mitchell, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro Von Werra, Harm de Vries

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The Vendi Score: A Diversity Evaluation Metric for Machine Learning Dan Friedman, Adji Bousso Dieng

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Tight Conditions for When the NTK Approximation Is Valid Enric Boix-Adserà, Etai Littwin

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TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection Dennis Wagner, Tobias Michels, Florian C.F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft

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Towards a Defense Against Federated Backdoor Attacks Under Continuous Training Shuaiqi Wang, Jonathan Hayase, Giulia Fanti, Sewoong Oh

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Towards a General Transfer Approach for Policy-Value Networks Dennis J. N. J. Soemers, Vegard Mella, Eric Piette, Matthew Stephenson, Cameron Browne, Olivier Teytaud

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Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models Gregory Plumb, Nari Johnson, Angel Cabrera, Ameet Talwalkar

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Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks HyunGeun Lee, Kijung Yoon

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Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

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Towards Fair Video Summarization Anshuman Chhabra, Kartik Patwari, Chandana Kuntala, Sristi, Deepak Kumar Sharma, Prasant Mohapatra

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Towards Large Scale Transfer Learning for Differentially Private Image Classification Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

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Towards Multi-Spatiotemporal-Scale Generalized PDE Modeling Jayesh K Gupta, Johannes Brandstetter

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Towards Optimization-Friendly Binary Neural Network Nianhui Guo, Joseph Bethge, Hong Guo, Christoph Meinel, Haojin Yang

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Towards Stability of Autoregressive Neural Operators Michael McCabe, Peter Harrington, Shashank Subramanian, Jed Brown

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Training Data Size Induced Double Descent for Denoising Feedforward Neural Networks and the Role of Training Noise Rishi Sonthalia, Raj Rao Nadakuditi

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Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges Kamran Chitsaz, Goncalo Mordido, Jean-Pierre David, François Leduc-Primeau

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Training Vision-Language Transformers from Captions Liangke Gui, Yingshan Chang, Qiuyuan Huang, Subhojit Som, Alexander G Hauptmann, Jianfeng Gao, Yonatan Bisk

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Training with Mixed-Precision Floating-Point Assignments Wonyeol Lee, Rahul Sharma, Alex Aiken

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Transductive Decoupled Variational Inference for Few-Shot Classification Anuj Rajeeva Singh, Hadi Jamali-Rad

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Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards

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TransFool: An Adversarial Attack Against Neural Machine Translation Models Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard

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Transformer for Partial Differential Equations’ Operator Learning Zijie Li, Kazem Meidani, Amir Barati Farimani

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Transframer: Arbitrary Frame Prediction with Generative Models Charlie Nash, Joao Carreira, Jacob C Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter Battaglia

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Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang, David Blei, Christian A Naesseth

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Transport with Support: Data-Conditional Diffusion Bridges Ella Tamir, Martin Trapp, Arno Solin

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Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings Wenbin Li, Xuesong Yang, Meihao Kong, Lei Wang, Jing Huo, Yang Gao, Jiebo Luo

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TSMixer: An All-MLP Architecture for Time Series Forecast-Ing Si-An Chen, Chun-Liang Li, Sercan O Arik, Nathanael Christian Yoder, Tomas Pfister

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Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion Si Chen, Yi Zeng, Won Park, Jiachen T. Wang, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia

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Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows Guillaume Morel, Lucas Drumetz, Simon Benaïchouche, Nicolas Courty, François Rousseau

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Two-Level Actor-Critic Using Multiple Teachers Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor

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U-NO: U-Shaped Neural Operators Md Ashiqur Rahman, Zachary E Ross, Kamyar Azizzadenesheli

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U-Statistics for Importance-Weighted Variational Inference Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

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UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography Francisca Vasconcelos, Bobby He, Nalini M Singh, Yee Whye Teh

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Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antoran, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

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Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient Yuhang Li, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda

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Uncovering Unique Concept Vectors Through Latent Space Decomposition Mara Graziani, Laura O'Mahony, An-phi Nguyen, Henning Müller, Vincent Andrearczyk

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Undersampling Is a Minimax Optimal Robustness Intervention in Nonparametric Classification Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto

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Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks Zhen Xu, Quanming Yao, Yong Li, Qiang Yang

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Understanding Convolution on Graphs via Energies Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

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Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization Runlong Zhou, Zelin He, Yuandong Tian, Yi Wu, Simon Shaolei Du

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Understanding Noise-Augmented Training for Randomized Smoothing Ambar Pal, Jeremias Sulam

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Understanding Self-Supervised Pretraining with Part-Aware Representation Learning Jie Zhu, Jiyang Qi, Mingyu Ding, Xiaokang Chen, Ping Luo, Xinggang Wang, Wenyu Liu, Leye Wang, Jingdong Wang

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Understanding the Robustness Difference Between Stochastic Gradient Descent and Adaptive Gradient Methods Avery Ma, Yangchen Pan, Amir-massoud Farahmand

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Unifying Physical Systems’ Inductive Biases in Neural ODE Using Dynamics Constraints Yi Heng Lim, Muhammad Firmansyah Kasim

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UnIVAL: Unified Model for Image, Video, Audio and Language Tasks Mustafa Shukor, Corentin Dancette, Alexandre Rame, Matthieu Cord

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Universal Graph Continual Learning Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen, Bao-Sinh Nguyen, Huy Hoang Nguyen, Hung Le

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Unsupervised Discovery and Composition of Object Light Fields Cameron Omid Smith, Hong-Xing Yu, Sergey Zakharov, Fredo Durand, Joshua B. Tenenbaum, Jiajun Wu, Vincent Sitzmann

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Unsupervised Domain Adaptation via Minimized Joint Error Dexuan Zhang, Thomas Westfechtel, Tatsuya Harada

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Using Confounded Data in Latent Model-Based Reinforcement Learning Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer

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Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni

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V1T: Large-Scale Mouse V1 Response Prediction Using a Vision Transformer Bryan M. Li, Isabel Maria Cornacchia, Nathalie Rochefort, Arno Onken

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Variational Causal Dynamics: Discovering Modular World Models from Interventions Anson Lei, Bernhard Schölkopf, Ingmar Posner

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Variational Elliptical Processes Maria Margareta Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön

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Visualizing the Diversity of Representations Learned by Bayesian Neural Networks Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina MC Höhne

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ViViT: Curvature Access Through the Generalized Gauss-Newton’s Low-Rank Structure Felix Dangel, Lukas Tatzel, Philipp Hennig

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VN-Transformer: Rotation-Equivariant Attention for Vector Neurons Serge Assaad, Carlton Downey, Rami Al-Rfou', Nigamaa Nayakanti, Benjamin Sapp

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VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment Shraman Pramanick, Li Jing, Sayan Nag, Jiachen Zhu, Hardik J Shah, Yann LeCun, Rama Chellappa

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Vulnerability-Aware Instance Reweighting for Adversarial Training Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague, Jin Tian

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Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing Jan Tönshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe

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Weight-Balancing Fixes and Flows for Deep Learning Lawrence K. Saul

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Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, Chaim Baskin

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When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju

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When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning Wenkai Yang, Yankai Lin, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun

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WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad Javad Darvishi Bayazi, Pooneh Mousavi, Guillaume Dumas, Irina Rish

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Workflow Discovery from Dialogues in the Low Data Regime Amine El hattami, Issam H. Laradji, Stefania Raimondo, David Vazquez, Pau Rodriguez, Christopher Pal

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Worst-Case Feature Risk Minimization for Data-Efficient Learning Jingshi Lei, Da Li, Chengming Xu, Liming Fang, Timothy Hospedales, Yanwei Fu

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Wrapped $\beta$-Gaussians with Compact Support for Exact Probabilistic Modeling on Manifolds Sergey Troshin, Vlad Niculae

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You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction Wenqing Zheng, Edward W Huang, Nikhil Rao, Zhangyang Wang, Karthik Subbian

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Zero-Shot Node Classification with Graph Contrastive Embedding Network Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, Ming Zhang

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