ICML 2022
1231 papers
3D Infomax Improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, Zico Kolter A Closer Look at Smoothness in Domain Adversarial Training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan A Consistent and Efficient Evaluation Strategy for Attribution Methods
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng A Data-Driven Approach for Learning to Control Computers
Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy Lillicrap A Deep Convolutional Neural Network That Is Invariant to Time Rescaling
Brandon G Jacques, Zoran Tiganj, Aakash Sarkar, Marc Howard, Per Sederberg A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Lukas Wolf, Ard Kastrati, Martyna B Plomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer A Differential Entropy Estimator for Training Neural Networks
Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida A Dynamical System Perspective for Lipschitz Neural Networks
Laurent Meunier, Blaise J Delattre, Alexandre Araujo, Alexandre Allauzen A General Recipe for Likelihood-Free Bayesian Optimization
Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon A Joint Exponential Mechanism for Differentially Private Top-$k$
Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi, Emmanuel De Bézenac, Ibrahim Ayed, Mickael Chen, Sylvain Lamprier, Patrick Gallinari A Psychological Theory of Explainability
Scott Cheng-Hsin Yang, Nils Erik Tomas Folke, Patrick Shafto A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan A Simple Guard for Learned Optimizers
Isabeau Prémont-Schwarz, Jaroslav Vı́tků, Jan Feyereisl A Statistical Manifold Framework for Point Cloud Data
Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Park A Study of Face Obfuscation in ImageNet
Kaiyu Yang, Jacqueline H. Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky A Study on the Ramanujan Graph Property of Winning Lottery Tickets
Bithika Pal, Arindam Biswas, Sudeshna Kolay, Pabitra Mitra, Biswajit Basu A Temporal-Difference Approach to Policy Gradient Estimation
Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood Accelerated Federated Learning with Decoupled Adaptive Optimization
Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson Accelerating Shapley Explanation via Contributive Cooperator Selection
Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang Active Fairness Auditing
Tom Yan, Chicheng Zhang Active Nearest Neighbor Regression Through Delaunay Refinement
Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic Active Sampling for Min-Max Fairness
Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang ActiveHedge: Hedge Meets Active Learning
Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama Actor-Critic Based Improper Reinforcement Learning
Mohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie Mannor AdaGrad Avoids Saddle Points
Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras, Xiao Wang Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato Adaptive Conformal Predictions for Time Series
Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut Adaptive Data Analysis with Correlated Observations
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer Adaptive Gaussian Process Change Point Detection
Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause Adaptive Model Design for Markov Decision Process
Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang AdAUC: End-to-End Adversarial AUC Optimization Against Long-Tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming Huang Additive Gaussian Processes Revisited
Xiaoyu Lu, Alexis Boukouvalas, James Hensman Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam R Villaflor, Zhe Huang, Swapnil Pande, John M Dolan, Jeff Schneider Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli Adversarial Masking for Self-Supervised Learning
Yuge Shi, N Siddharth, Philip Torr, Adam R Kosiorek AGNAS: Attention-Guided Micro and Macro-Architecture Search
Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp Align-RUDDER: Learning from Few Demonstrations by Reward Redistribution
Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies, Johannes Brandstetter, José Arjona-Medina, Sepp Hochreiter An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud An Intriguing Property of Geophysics Inversion
Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin Analyzing and Mitigating Interference in Neural Architecture Search
Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu Anticorrelated Noise Injection for Improved Generalization
Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi Asymptotically-Optimal Gaussian Bandits with Side Observations
Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B Paulus, Yangjun Ruan, Chris Maddison AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long, Zheng Wang, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael Mahoney AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon BabelTower: Learning to Auto-Parallelized Program Translation
Yuanbo Wen, Qi Guo, Qiang Fu, Xiaqing Li, Jianxing Xu, Yanlin Tang, Yongwei Zhao, Xing Hu, Zidong Du, Ling Li, Chao Wang, Xuehai Zhou, Yunji Chen Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, Venkatesh Babu Radhakrishnan Batched Dueling Bandits
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan Bayesian Continuous-Time Tucker Decomposition
Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe Bayesian Deep Embedding Topic Meta-Learner
Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, Wenchao Chen, Chaojie Wang, Mingyuan Zhou Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson Bayesian Nonparametrics for Offline Skill Discovery
Valentin Villecroze, Harry Braviner, Panteha Naderian, Chris Maddison, Gabriel Loaiza-Ganem Be like Water: Adaptive Floating Point for Machine Learning
Thomas Yeh, Max Sterner, Zerlina Lai, Brandon Chuang, Alexander Ihler Being Properly Improper
Tyler Sypherd, Richard Nock, Lalitha Sankar Biological Sequence Design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine Black-Box Tuning for Language-Model-as-a-Service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch Bregman Neural Networks
Jordan Frecon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo Building Robust Ensembles via Margin Boosting
Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan Causal Conceptions of Fairness and Their Consequences
Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, Sharad Goel Causal Inference Through the Structural Causal Marginal Problem
Luigi Gresele, Julius Von Kügelgen, Jonas Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing Causal Structure-Based Root Cause Analysis of Outliers
Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing Certified Neural Network Watermarks with Randomized Smoothing
Arpit Bansal, Ping-Yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, Tom Goldstein Certified Robustness Against Natural Language Attacks by Causal Intervention
Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang Certifying Out-of-Domain Generalization for Blackbox Functions
Maurice G Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang CITRIS: Causal Identifiability from Temporal Intervened Sequences
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Stratis Gavves Coin Flipping Neural Networks
Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster COLA: Consistent Learning with Opponent-Learning Awareness
Timon Willi, Alistair Hp Letcher, Johannes Treutlein, Jakob Foerster Combining Diverse Feature Priors
Saachi Jain, Dimitris Tsipras, Aleksander Madry Communicating via Markov Decision Processes
Samuel Sokota, Christian A Schroeder De Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, Martin Strohmeier, Zico Kolter, Shimon Whiteson, Jakob Foerster Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz Conditional GANs with Auxiliary Discriminative Classifier
Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng Conformal Prediction Sets with Limited False Positives
Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay Congested Bandits: Optimal Routing via Short-Term Resets
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen, Robbie M Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. Haochen, Tengyu Ma, Percy Liang Consistent Polyhedral Surrogates for Top-K Classification and Variants
Anish Thilagar, Rafael Frongillo, Jessica J Finocchiaro, Emma Goodwill Constrained Discrete Black-Box Optimization Using Mixed-Integer Programming
Theodore P Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger Constrained Offline Policy Optimization
Nicholas Polosky, Bruno C. Da Silva, Madalina Fiterau, Jithin Jagannath Constrained Optimization with Dynamic Bound-Scaling for Effective NLP Backdoor Defense
Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, Xiangyu Zhang Constrained Variational Policy Optimization for Safe Reinforcement Learning
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li, Ding Zhao Constraint-Based Graph Network Simulator
Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David Cox, Mark Hasegawa-Johnson, Shiyu Chang Context-Aware Drift Detection
Oliver Cobb, Arnaud Van Looveren Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk, Karol Piczak, Bartosz Wójcik, Lukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzcinski, Przemysław Spurek Continual Repeated Annealed Flow Transport Monte Carlo
Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet Continuous Control with Action Quantization from Demonstrations
Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin Contrastive Learning with Boosted Memorization
Zhihan Zhou, Jiangchao Yao, Yan-Feng Wang, Bo Han, Ya Zhang Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui Curriculum Reinforcement Learning via Constrained Optimal Transport
Pascal Klink, Haoyi Yang, Carlo D’Eramo, Jan Peters, Joni Pajarinen Data Augmentation as Feature Manipulation
Ruoqi Shen, Sebastien Bubeck, Suriya Gunasekar Data Determines Distributional Robustness in Contrastive Language Image Pre-Training (CLIP)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat Data-Efficient Double-Win Lottery Tickets from Robust Pre-Training
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang Datamodels: Understanding Predictions with Data and Data with Predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song Dataset Condensation with Contrastive Signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model
Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S Noble Decentralized Online Convex Optimization in Networked Systems
Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman Decision-Focused Learning: Through the Lens of Learning to Rank
Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning
Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao Deep and Flexible Graph Neural Architecture Search
Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, Bin Cui Deep Causal Metric Learning
Xiang Deng, Zhongfei Zhang Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi, Riccardo Zecchina Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda Deep Symbolic Regression for Recurrence Prediction
Stéphane D’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, Francois Charton DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He Delay-Adaptive Step-Sizes for Asynchronous Learning
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, Mikael Johansson Delayed Reinforcement Learning by Imitation
Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli Deletion Robust Submodular Maximization over Matroids
Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam Denoised MDPs: Learning World Models Better than the World Itself
Tongzhou Wang, Simon Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu Differentiable Top-K Classification Learning
Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen Diffusion Bridges Vector Quantized Variational Autoencoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar Direct Behavior Specification via Constrained Reinforcement Learning
Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Chris J Pal Distributionally Robust $q$-Learning
Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou Diversified Adversarial Attacks Based on Conjugate Gradient Method
Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Toru Mitsutake, Issa Oe, Hiroki Ishikura, Katsuki Fujisawa DNA: Domain Generalization with Diversified Neural Averaging
Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, Hong Mei Do Differentiable Simulators Give Better Policy Gradients?
Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang Double Sampling Randomized Smoothing
Linyi Li, Jiawei Zhang, Tao Xie, Bo Li DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks
Zhuang Wang, Zhaozhuo Xu, Xinyu Wu, Anshumali Shrivastava, T. S. Eugene Ng DynaMixer: A Vision MLP Architecture with Dynamic Mixing
Ziyu Wang, Wenhao Jiang, Yiming M Zhu, Li Yuan, Yibing Song, Wei Liu EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben Itzhak, Michael Mitzenmacher Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima Efficient Distributionally Robust Bayesian Optimization with Worst-Case Sensitivity
Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low Efficient Learning of CNNs Using Patch Based Features
Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Schwartz Efficient Representation Learning via Adaptive Context Pooling
Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M Susskind Efficient Test-Time Model Adaptation Without Forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan Efficiently Learning the Topology and Behavior of a Networked Dynamical System via Active Queries
Daniel J Rosenkrantz, Abhijin Adiga, Madhav Marathe, Zirou Qiu, S S Ravi, Richard Stearns, Anil Vullikanti Entropic Causal Inference: Graph Identifiability
Spencer Compton, Kristjan Greenewald, Dmitriy A Katz, Murat Kocaoglu Entropic Gromov-Wasserstein Between Gaussian Distributions
Khang Le, Dung Q Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola Equivariance Versus Augmentation for Spherical Images
Jan Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling Equivariant Quantum Graph Circuits
Peter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan Evaluating the Adversarial Robustness of Adaptive Test-Time Defenses
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, Taylan Cemgil Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, Orhan Firat Fair and Fast K-Center Clustering for Data Summarization
Haris Angelidakis, Adam Kurpisz, Leon Sering, Rico Zenklusen Fair Generalized Linear Models with a Convex Penalty
Hyungrok Do, Preston Putzel, Axel S Martin, Padhraic Smyth, Judy Zhong Fair Representation Learning Through Implicit Path Alignment
Changjian Shui, Qi Chen, Jiaqi Li, Boyu Wang, Christian Gagné Fairness Interventions as (Dis)Incentives for Strategic Manipulation
Xueru Zhang, Mohammad Mahdi Khalili, Kun Jin, Parinaz Naghizadeh, Mingyan Liu Fast and Provable Nonconvex Tensor RPCA
Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava, John Z Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert Kevin Katzschmann Fast Finite Width Neural Tangent Kernel
Roman Novak, Jascha Sohl-Dickstein, Samuel S Schoenholz Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot Fast Provably Robust Decision Trees and Boosting
Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou Fast Relative Entropy Coding with A* Coding
Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato Faster Algorithms for Learning Convex Functions
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L Geyer, Venkatesh Saligrama, Brian Kulis Faster Privacy Accounting via Evolving Discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh Feature Selection Using E-Values
Subhabrata Majumdar, Snigdhansu Chatterjee Feature Space Particle Inference for Neural Network Ensembles
Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao Federated Learning with Positive and Unlabeled Data
Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, Mosharaf Chowdhury Fighting Fire with Fire: Avoiding DNN Shortcuts Through Priming
Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob D Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert Flow-Based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long For Learning in Symmetric Teams, Local Optima Are Global Nash Equilibria
Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell Forget-Free Continual Learning with Winning Subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo Fourier Learning with Cyclical Data
Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang FriendlyCore: Practical Differentially Private Aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer From Block-Toeplitz Matrices to Differential Equations on Graphs: Towards a General Theory for Scalable Masked Transformers
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Darius Muglich, Luisa M Zintgraf, Christian A Schroeder De Witt, Shimon Whiteson, Jakob Foerster Generalizing to New Physical Systems via Context-Informed Dynamics Model
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Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan C Cebere, Tennison Liu, Alicia Curth, Mihaela Schaar HyperPrompt: Prompt-Based Task-Conditioning of Transformers
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David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause Interpretable Off-Policy Learning via Hyperbox Search
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Phillip B Mogensen, Nikolaj Thams, Jonas Peters Investigating Generalization by Controlling Normalized Margin
Alexander R Farhang, Jeremy D Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue It’s Raw! Audio Generation with State-Space Models
Karan Goel, Albert Gu, Chris Donahue, Christopher Re Knowledge Base Question Answering by Case-Based Reasoning over Subgraphs
Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew Mccallum Label Ranking Through Nonparametric Regression
Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar Large Batch Experience Replay
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Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu Latent Diffusion Energy-Based Model for Interpretable Text Modelling
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Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang Learning Inverse Folding from Millions of Predicted Structures
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Max B Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris Maddison Learning to Infer Structures of Network Games
Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
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Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor, Adam Golinski LIMO: Latent Inceptionism for Targeted Molecule Generation
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Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan D Cotterell Linear Bandit Algorithms with Sublinear Time Complexity
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Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu Low-Precision Stochastic Gradient Langevin Dynamics
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Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason J Choi, Michael Janner, Claire Tomlin, Sergey Levine Making Linear MDPs Practical via Contrastive Representation Learning
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Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt Model-Free Opponent Shaping
Christopher Lu, Timon Willi, Christian A Schroeder De Witt, Jakob Foerster Model-Value Inconsistency as a Signal for Epistemic Uncertainty
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Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown Modeling Structure with Undirected Neural Networks
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Tri Dao, Beidi Chen, Nimit S Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Re Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh Multi-Scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie Multi-Task Learning as a Bargaining Game
Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya Multicoated Supermasks Enhance Hidden Networks
Yasuyuki Okoshi, Ángel López Garcı́a-Arias, Kazutoshi Hirose, Kota Ando, Kazushi Kawamura, Thiem Van Chu, Masato Motomura, Jaehoon Yu Multirate Training of Neural Networks
Tiffany J Vlaar, Benedict Leimkuhler NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui Nearly Optimal Catoni’s M-Estimator for Infinite Variance
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Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du, Jiantao Jiao Nested Bandits
Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati Neural Inverse Kinematic
Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf Neural Language Models Are Not Born Equal to Fit Brain Data, but Training Helps
Alexandre Pasquiou, Yair Lakretz, John T Hale, Bertrand Thirion, Christophe Pallier Neural Network Poisson Models for Behavioural and Neural Spike Train Data
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Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou NOMU: Neural Optimization-Based Model Uncertainty
Jakob M Heiss, Jakob Weissteiner, Hanna S Wutte, Sven Seuken, Josef Teichmann Nonlinear Feature Diffusion on Hypergraphs
Konstantin Prokopchik, Austin R Benson, Francesco Tudisco Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe NP-Match: When Neural Processes Meet Semi-Supervised Learning
Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Alexandros Neophytou Nyström Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
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Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H Pong, Ashvin V Nair, Laura M Smith, Catherine Huang, Sergey Levine Offline RL Policies Should Be Trained to Be Adaptive
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Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, Roger B Grosse On Last-Iterate Convergence Beyond Zero-Sum Games
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Ricardo Dominguez-Olmedo, Amir H Karimi, Bernhard Schölkopf On the Difficulty of Defending Self-Supervised Learning Against Model Extraction
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Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang On the Practicality of Deterministic Epistemic Uncertainty
Janis Postels, Mattia Segù, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, Federico Tombari On the Robustness of CountSketch to Adaptive Inputs
Edith Cohen, Xin Lyu, Jelani Nelson, Tamas Sarlos, Moshe Shechner, Uri Stemmer On Transportation of Mini-Batches: A Hierarchical Approach
Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams
Benjamin Coleman, Benito Geordie, Li Chou, R. A. Leo Elworth, Todd Treangen, Anshumali Shrivastava Online Active Regression
Cheng Chen, Yi Li, Yiming Sun Online Algorithms with Multiple Predictions
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi Online and Consistent Correlation Clustering
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis Online Balanced Experimental Design
David Arbour, Drew Dimmery, Tung Mai, Anup Rao Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette Optimization-Induced Graph Implicit Nonlinear Diffusion
Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin Order Constraints in Optimal Transport
Yu Chin Fabian Lim, Laura Wynter, Shiau Hong Lim Overcoming Oscillations in Quantization-Aware Training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort PAC-Net: A Model Pruning Approach to Inductive Transfer Learning
Sanghoon Myung, In Huh, Wonik Jang, Jae Myung Choe, Jisu Ryu, Daesin Kim, Kee-Eung Kim, Changwook Jeong Parsimonious Learning-Augmented Caching
Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit Partial Disentanglement for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang Particle Transformer for Jet Tagging
Huilin Qu, Congqiao Li, Sitian Qian Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules
Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee, Honglak Lee, Yousung Jung Path-Gradient Estimators for Continuous Normalizing Flows
Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Mayee Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Re Personalized Federated Learning Through Local Memorization
Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni Planning with Diffusion for Flexible Behavior Synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, Sergey Levine PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, Kristian Kersting Plug-in Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma PoF: Post-Training of Feature Extractor for Improving Generalization
Ikuro Sato, Yamada Ryota, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Y Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua M Susskind Power-Law Escape Rate of SGD
Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner Predicting Out-of-Distribution Error with the Projection Norm
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt Principal Component Flows
Edmond Cunningham, Adam D Cobb, Susmit Jha Principled Knowledge Extrapolation with GANs
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Tian Li, Manzil Zaheer, Sashank Reddi, Virginia Smith Private Frequency Estimation via Projective Geometry
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Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen M Mcaleer, Vlad Firoiu, Lei M Zhang, Doina Precup, Shibl Mourad Proximal and Federated Random Reshuffling
Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik Proximal Exploration for Model-Guided Protein Sequence Design
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Nadiia Chepurko, Kenneth Clarkson, Lior Horesh, Honghao Lin, David Woodruff Re-Evaluating Word Mover’s Distance
Ryoma Sato, Makoto Yamada, Hisashi Kashima Reachability Constrained Reinforcement Learning
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Daniel Kramer, Philine L Bommer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
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James Benjamin Simon, Sajant Anand, Mike Deweese Revisiting the Effects of Stochasticity for Hamiltonian Samplers
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Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò Sample Efficient Learning of Predictors That Complement Humans
Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi Sanity Simulations for Saliency Methods
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, Haitham Ammar Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
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Niloy Biswas, Lester Mackey, Xiao-Li Meng Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Hu Xianghong, Jeffry Wicaksana, Eric Xing, Kwang-Ting Cheng SE(3) Equivariant Graph Neural Networks with Complete Local Frames
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Longxing Yang, Yu Hu, Shun Lu, Zihao Sun, Jilin Mei, Yinhe Han, Xiaowei Li Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde Selective Regression Under Fairness Criteria
Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W Wornell Self-Conditioning Pre-Trained Language Models
Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff Self-Organized Polynomial-Time Coordination Graphs
Qianlan Yang, Weijun Dong, Zhizhou Ren, Jianhao Wang, Tonghan Wang, Chongjie Zhang Self-Supervised Models Are Good Teaching Assistants for Vision Transformers
Haiyan Wu, Yuting Gao, Yinqi Zhang, Shaohui Lin, Yuan Xie, Xing Sun, Ke Li Set Based Stochastic Subsampling
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Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu, Karl D.D. Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa Sparse Invariant Risk Minimization
Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang Sparsity in Partially Controllable Linear Systems
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Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu SpeqNets: Sparsity-Aware Permutation-Equivariant Graph Networks
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Rylan Schaeffer, Yilun Du, Gabrielle K Liu, Ila Fiete StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
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Jeremiah Birrell, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu Structured Stochastic Gradient MCMC
Antonios Alexos, Alex J Boyd, Stephan Mandt Supervised Learning with General Risk Functionals
Liu Leqi, Audrey Huang, Zachary Lipton, Kamyar Azizzadenesheli Supervised Off-Policy Ranking
Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu Symmetric Machine Theory of Mind
Melanie Sclar, Graham Neubig, Yonatan Bisk Tell Me Why! Explanations Support Learning Relational and Causal Structure
Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison Tam, James Mcclelland, Chen Yan, Adam Santoro, Neil C Rabinowitz, Jane Wang, Felix Hill The Algebraic Path Problem for Graph Metrics
Enrique Fita Sanmartı́n, Sebastian Damrich, Fred Hamprecht The CLRS Algorithmic Reasoning Benchmark
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Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov The Geometry of Robust Value Functions
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor The Infinite Contextual Graph Markov Model
Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takac, Pavel Dvurechensky, Bin Gu The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville The Role of Deconfounding in Meta-Learning
Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei The State of Sparse Training in Deep Reinforcement Learning
Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro The Teaching Dimension of Regularized Kernel Learners
Hong Qian, Xu-Hui Liu, Chen-Xi Su, Aimin Zhou, Yang Yu The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta Thresholded Lasso Bandit
Kaito Ariu, Kenshi Abe, Alexandre Proutiere Time Is MattEr: Temporal Self-Supervision for Video Transformers
Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, Jinwoo Shin To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu Topology-Aware Generalization of Decentralized SGD
Tongtian Zhu, Fengxiang He, Lan Zhang, Zhengyang Niu, Mingli Song, Dacheng Tao Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm H Van Seijen Towards Scaling Difference Target Propagation by Learning Backprop Targets
Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner, Florian Hess, Jonas M Mikhaeil, Leonard F Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz Tractable Uncertainty for Structure Learning
Benjie Wang, Matthew R Wicker, Marta Kwiatkowska Training OOD Detectors in Their Natural Habitats
Julian Katz-Samuels, Julia B Nakhleh, Robert Nowak, Yixuan Li Training Your Sparse Neural Network Better with Any Mask
Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-Time Retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N Gomez, Debora Marks, Yarin Gal Transformer Quality in Linear Time
Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc Le Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh TSPipe: Learn from Teacher Faster with Pipelines
Hwijoon Lim, Yechan Kim, Sukmin Yun, Jinwoo Shin, Dongsu Han UAST: Uncertainty-Aware Siamese Tracking
Dawei Zhang, Yanwei Fu, Zhonglong Zheng Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy Understanding Doubly Stochastic Clustering
Tianjiao Ding, Derek Lim, Rene Vidal, Benjamin D Haeffele Understanding Robust Overfitting of Adversarial Training and Beyond
Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu Understanding the Robustness in Vision Transformers
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, Jose M. Alvarez Unified Scaling Laws for Routed Language Models
Aidan Clark, Diego De Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, George Bm Van Den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc’Aurelio Ranzato, Jack Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan UniRank: Unimodal Bandit Algorithms for Online Ranking
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc Van Gool Validating Causal Inference Methods
Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen Variational Feature Pyramid Networks
Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou Variational On-the-Fly Personalization
Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak Variational Wasserstein Gradient Flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
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Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang VLMixer: Unpaired Vision-Language Pre-Training via Cross-Modal CutMix
Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo Weisfeiler-Lehman Meets Gromov-Wasserstein
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Yuxin Wang, Chu-Tak Lee, Qipeng Guo, Zhangyue Yin, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel When and How Mixup Improves Calibration
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