NeurIPS 2022
2834 papers
(De-)Randomized Smoothing for Decision Stump Ensembles
Miklós Horváth, Mark Müller, Marc Fischer, Martin Vechev 3D Concept Grounding on Neural Fields
Yining Hong, Yilun Du, Chunru Lin, Josh Tenenbaum, Chuang Gan 3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry 4D Unsupervised Object Discovery
Yuqi Wang, Yuntao Chen, Zhao-Xiang Zhang A Benchmark for Compositional Visual Reasoning
Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, Thomas Serre A Causal Analysis of Harm
Sander Beckers, Hana Chockler, Joseph Halpern A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet A Contrastive Framework for Neural Text Generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, Nigel Collier A Contrastive Rule for Meta-Learning
Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento A Deep Learning Dataloader with Shared Data Preparation
Jian Xie, Jingwei Xu, Guochang Wang, Yuan Yao, Zenan Li, Chun Cao, Hanghang Tong A Deep Reinforcement Learning Framework for Column Generation
Cheng Chi, Amine Aboussalah, Elias Khalil, Juyoung Wang, Zoha Sherkat-Masoumi A Fast Post-Training Pruning Framework for Transformers
Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami A Fourier Approach to Mixture Learning
Mingda Qiao, Guru Guruganesh, Ankit Rawat, Kumar Avinava Dubey, Manzil Zaheer A General Framework for Auditing Differentially Private Machine Learning
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa A Lagrangian Duality Approach to Active Learning
Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro A Large Scale Search Dataset for Unbiased Learning to Rank
Lixin Zou, Haitao Mao, Xiaokai Chu, Jiliang Tang, Wenwen Ye, Shuaiqiang Wang, Dawei Yin A Lower Bound of Hash Codes' Performance
Xiaosu Zhu, Jingkuan Song, Yu Lei, Lianli Gao, Hengtao Shen A Mean-Field Game Approach to Cloud Resource Management with Function Approximation
Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk, Ravishankar Iyer, Tamer Basar A Mixture of Surprises for Unsupervised Reinforcement Learning
Andrew Zhao, Matthieu Lin, Yangguang Li, Yong-jin Liu, Gao Huang A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Fabian Falck, Christopher K. I. Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C Holmes, Arnaud Doucet, Matthew Willetts A Neural Corpus Indexer for Document Retrieval
Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Seo Taek Kong, Soomin Jeon, Dongbin Na, Jaewon Lee, Hong-Seok Lee, Kyu-Hwan Jung A Permutation-Free Kernel Two-Sample Test
Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas A Probabilistic Graph Coupling View of Dimension Reduction
Hugues Van Assel, Thibault Espinasse, Julien Chiquet, Franck Picard A Regret-Variance Trade-Off in Online Learning
Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi A Simple Approach to Automated Spectral Clustering
Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang A Simple but Strong Baseline for Online Continual Learning: Repeated Augmented Rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand, Jun Luo, Dale Schuurmans A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong A Theoretical Framework for Inference Learning
Nick Alonso, Beren Millidge, Jeffrey Krichmar, Emre O Neftci A Theoretical Study on Solving Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, Bing Liu A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang A Theoretical View on Sparsely Activated Networks
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang A Theory of Weight Distribution-Constrained Learning
Weishun Zhong, Ben Sorscher, Daniel D. Lee, Haim Sompolinsky A Unified Framework for Deep Symbolic Regression
Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Claudio P Santiago, Ignacio Aravena, Terrell Mundhenk, Garrett Mulcahy, Brenden K Petersen A Unified Model for Multi-Class Anomaly Detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le A Unified Sequence Interface for Vision Tasks
Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J Fleet, Geoffrey E. Hinton A Unifying Framework for Online Optimization with Long-Term Constraints
Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, Nicola Gatti A Universal Error Measure for Input Predictions Applied to Online Graph Problems
Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering A Win-Win Deal: Towards Sparse and Robust Pre-Trained Language Models
Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou A2: Efficient Automated Attacker for Boosting Adversarial Training
Zhuoer Xu, Guanghui Zhu, Changhua Meng, Shiwen Cui, Zhenzhe Ying, Weiqiang Wang, Ming Gu, Yihua Huang Accelerating Sparse Convolution with Column Vector-Wise Sparsity
Yijun Tan, Kai Han, Kang Zhao, Xianzhi Yu, Zidong Du, Yunji Chen, Yunhe Wang, Jun Yao Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen Active Labeling: Streaming Stochastic Gradients
Vivien Cabannes, Francis R. Bach, Vianney Perchet, Alessandro Rudi Active Learning Helps Pretrained Models Learn the Intended Task
Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah Goodman Active Learning of Classifiers with Label and Seed Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen Active Learning Polynomial Threshold Functions
Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu Active Learning Through a Covering Lens
Ofer Yehuda, Avihu Dekel, Guy Hacohen, Daphna Weinshall Active Learning with Safety Constraints
Romain Camilleri, Andrew Wagenmaker, Jamie H Morgenstern, Lalit Jain, Kevin G. Jamieson Active Ranking Without Strong Stochastic Transitivity
Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud Adam Can Converge Without Any Modification on Update Rules
Yushun Zhang, Congliang Chen, Naichen Shi, Ruoyu Sun, Zhi-Quan Luo AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, Ping Luo Adapting to Online Label Shift with Provable Guarantees
Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou Adaptive Interest for Emphatic Reinforcement Learning
Martin Klissarov, Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Taesup Kim, Alexander J Smola Adaptive Oracle-Efficient Online Learning
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy Adaptive Sampling for Discovery
Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman Adaptive Stochastic Variance Reduction for Non-Convex Finite-Sum Minimization
Ali Kavis, Stratis Skoulakis, Kimon Antonakopoulos, Leello Tadesse Dadi, Volkan Cevher ADBench: Anomaly Detection Benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, Yue Zhao Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Syed Ashar Javed, Dinkar Juyal, Harshith Padigela, Amaro Taylor-Weiner, Limin Yu, Aaditya Prakash Addressing Leakage in Concept Bottleneck Models
Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez Adjoint-Aided Inference of Gaussian Process Driven Differential Equations
Paterne Gahungu, Christopher Lanyon, Mauricio A Álvarez, Engineer Bainomugisha, Michael T Smith, Richard Wilkinson Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, Chao Ma Advancing Model Pruning via Bi-Level Optimization
Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao Adversarial Robustness Is at Odds with Lazy Training
Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora Adversarial Training for High-Stakes Reliability
Daniel Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima, Benjamin Weinstein-Raun, Daniel de Haas, Buck Shlegeris, Nate Thomas Algorithms with Prediction Portfolios
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii Aligning Individual Brains with Fused Unbalanced Gromov Wasserstein
Alexis Thual, Quang Huy Tran, Tatiana Zemskova, Nicolas Courty, Rémi Flamary, Stanislas Dehaene, Bertrand Thirion All Politics Is Local: Redistricting via Local Fairness
Shao-Heng Ko, Erin Taylor, Pankaj Agarwal, Kamesh Munagala Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient
Yewen Li, Chaojie Wang, Zhibin Duan, Dongsheng Wang, Bo Chen, Bo An, Mingyuan Zhou Amortized Inference for Causal Structure Learning
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Axel Levy, Gordon Wetzstein, Julien N.P Martel, Frederic Poitevin, Ellen Zhong Amortized Proximal Optimization
Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B Grosse AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Yuanfeng Ji, Haotian Bai, Chongjian Ge, Jie Yang, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhanng, Wanling Ma, Xiang Wan, Ping Luo Amplifying Membership Exposure via Data Poisoning
Yufei Chen, Chao Shen, Yun Shen, Cong Wang, Yang Zhang An $\alpha$-No-Regret Algorithm for Graphical Bilinear Bandits
Geovani Rizk, Igor Colin, Albert Thomas, Rida Laraki, Yann Chevaleyre An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu An Algorithm for Learning Switched Linear Dynamics from Data
Guillaume Berger, Monal Narasimhamurthy, Kandai Watanabe, Morteza Lahijanian, Sriram Sankaranarayanan An Analysis of Ensemble Sampling
Chao Qin, Zheng Wen, Xiuyuan Lu, Benjamin Van Roy An Empirical Analysis of Compute-Optimal Large Language Model Training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, Laurent Sifre An In-Depth Study of Stochastic Backpropagation
Jun Fang, Mingze Xu, Hao Chen, Bing Shuai, Zhuowen Tu, Joseph Tighe An Investigation into Whitening Loss for Self-Supervised Learning
Xi Weng, Lei Huang, Lei Zhao, Rao Anwer, Salman H Khan, Fahad Shahbaz Khan Analyzing Data-Centric Properties for Graph Contrastive Learning
Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, Jayaraman Thiagarajan Anonymized Histograms in Intermediate Privacy Models
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi Anonymous Bandits for Multi-User Systems
Hossein Esfandiari, Vahab Mirrokni, Jon Schneider AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Marius Dragoi, Elena Burceanu, Emanuela Haller, Andrei Manolache, Florin Brad APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, Bo Zheng Approximate Value Equivalence
Christopher Grimm, Andre Barreto, Satinder P. Singh Are All Losses Created Equal: A Neural Collapse Perspective
Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh Are Defenses for Graph Neural Networks Robust?
Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski Are GANs Overkill for NLP?
David Alvarez-Melis, Vikas Garg, Adam Kalai Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell Ask4Help: Learning to Leverage an Expert for Embodied Tasks
Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi, Aniruddha Kembhavi, Roozbeh Mottaghi Assaying Out-of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello Assistive Teaching of Motor Control Tasks to Humans
Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah Goodman, Dorsa Sadigh Associating Objects and Their Effects in Video Through Coordination Games
Erika Lu, Forrester Cole, Weidi Xie, Tali Dekel, Bill Freeman, Andrew Zisserman, Michael Rubinstein Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Xin-Chun Li, Wen-shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Shao Yunfeng, De-Chuan Zhan Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E Woodworth ATD: Augmenting CP Tensor Decomposition by Self Supervision
Chaoqi Yang, Cheng Qian, Navjot Singh, Cao Xiao, M Westover, Edgar Solomonik, Jimeng Sun AttCAT: Explaining Transformers via Attentive Class Activation Tokens
Yao Qiang, Deng Pan, Chengyin Li, Xin Li, Rhongho Jang, Dongxiao Zhu Attention-Based Neural Cellular Automata
Mattie Tesfaldet, Derek Nowrouzezahrai, Chris Pal Audio-Driven Co-Speech Gesture Video Generation
Xian Liu, Qianyi Wu, Hang Zhou, Yuanqi Du, Wayne Wu, Dahua Lin, Ziwei Liu Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang Autoformalization with Large Language Models
Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy Autoinverse: Uncertainty Aware Inversion of Neural Networks
Navid Ansari, Hans-peter Seidel, Nima Vahidi Ferdowsi, Vahid Babaei AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-Parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti Lnu, Ganesh Ramakrishnan, Alexandre Evfimievski, Lucian Popa, Rishabh Iyer Automatic Differentiation of Programs with Discrete Randomness
Gaurav Arya, Moritz Schauer, Frank Schäfer, Christopher Rackauckas AutoML Two-Sample Test
Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf Autoregressive Perturbations for Data Poisoning
Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs Autoregressive Search Engines: Generating Substrings as Document Identifiers
Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Scott Yih, Sebastian Riedel, Fabio Petroni AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
Joshua Albrecht, Abraham Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zack Polizzi, James Simon, Kanjun Qiu BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen BadPrompt: Backdoor Attacks on Continuous Prompts
Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, Yuan Xiaojie Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvari, Mengdi Wang Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li, Jeff M Phillips, Xin Yu, Robert Kirby, Shandian Zhe Bayesian Active Learning with Fully Bayesian Gaussian Processes
Christoffer Riis, Francisco Antunes, Frederik Hüttel, Carlos Lima Azevedo, Francisco Pereira Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, Eytan Bakshy Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Kim, Ameet Talwalkar, Hoda Heidari, Steven Z. Wu Behavior Transformers: Cloning $k$ Modes with One Stone
Nur Muhammad Shafiullah, Zichen Cui, Ariuntuya Altanzaya, Lerrel Pinto Benchopt: Reproducible, Efficient and Collaborative Optimization Benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupre la Tour, Ghislain Durif, Cassio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter Benefits of Permutation-Equivariance in Auction Mechanisms
Tian Qin, Fengxiang He, Dingfeng Shi, Wenbing Huang, Dacheng Tao Benign Underfitting of Stochastic Gradient Descent
Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting
Neil Mallinar, James Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
Joshua Mitton, Simon Mekhail, Miles Padgett, Daniele Faccio, Marco Aversa, Roderick Murray-Smith Best of Both Worlds Model Selection
Aldo Pacchiano, Christoph Dann, Claudio Gentile BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang, Bing Wang, Zhi Tang Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak, Shahab Asoodeh, Flavio Calmon Beyond L1: Faster and Better Sparse Models with Skglm
Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier, Gauthier Gidel, Mathurin Massias Beyond Mahalanobis Distance for Textual OOD Detection
Pierre Colombo, Eduardo Dadalto, Guillaume Staerman, Nathan Noiry, Pablo Piantanida Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari Morcos BigBio: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Sunny Kang, Rosaline Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen Bach, Stella Biderman, Mario Sänger, Bo Wang, Alison Callahan, Daniel León Periñán, Théo Gigant, Patrick Haller, Jenny Chim, Jose Posada, John Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Broad, Yanis Labrak, Shlok Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Xuelong Mi, Mengfan Wang, Alex Chen, Jing-Xuan Lim, Yizhi Wang, Misha B Ahrens, Guoqiang Yu BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo P. Mandic, Lei He, Xiangyang Li, Tao Qin, Sheng Zhao, Tie-Yan Liu Biological Learning of Irreducible Representations of Commuting Transformations
Alexander Genkin, David Lipshutz, Siavash Golkar, Tiberiu Tesileanu, Dmitri B. Chklovskii Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
Jianchuan Ding, Bo Dong, Felix Heide, Yufei Ding, Yunduo Zhou, Baocai Yin, Xin Yang BiT: Robustly Binarized Multi-Distilled Transformer
Zechun Liu, Barlas Oguz, Aasish Pappu, Lin Xiao, Scott Yih, Meng Li, Raghuraman Krishnamoorthi, Yashar Mehdad Black-Box Coreset Variational Inference
Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos Black-Box Generalization: Stability of Zeroth-Order Learning
Konstantinos Nikolakakis, Farzin Haddadpour, Dionysis Kalogerias, Amin Karbasi Blackbox Attacks via Surrogate Ensemble Search
Zikui Cai, Chengyu Song, Srikanth Krishnamurthy, Amit Roy-Chowdhury, Salman Asif Block-Recurrent Transformers
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur BLOX: Macro Neural Architecture Search Benchmark and Algorithms
Thomas Chau, Łukasz Dudziak, Hongkai Wen, Nicholas Lane, Mohamed Abdelfattah BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H Chen, Zhihao Jia, Philip S Yu Boosting Out-of-Distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue', Xiang Tian, Bolun Zheng, Yaowu Chen Bootstrapped Transformer for Offline Reinforcement Learning
Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, Dongsheng Li BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson Bringing Image Scene Structure to Video via Frame-CLIP Consistency of Object Tokens
Elad Ben Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson BYOL-Explore: Exploration by Bootstrapped Prediction
Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Remi Munos, Mohammad Gheshlaghi Azar, Bilal Piot Byzantine Spectral Ranking
Arnhav Datar, Arun Rajkumar, John Augustine C-Mixup: Improving Generalization in Regression
Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y Zou, Chelsea Finn CageNeRF: Cage-Based Neural Radiance Field for Generalized 3D Deformation and Animation
Yicong Peng, Yichao Yan, Shengqi Liu, Yuhao Cheng, Shanyan Guan, Bowen Pan, Guangtao Zhai, Xiaokang Yang CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, Liwei Wang Can Adversarial Training Be Manipulated by Non-Robust Features?
Lue Tao, Lei Feng, Hongxin Wei, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen Can Push-Forward Generative Models Fit Multimodal Distributions?
Antoine Salmona, Valentin De Bortoli, Julie Delon, Agnes Desolneux Capturing Graphs with Hypo-Elliptic Diffusions
Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser CASA: Category-Agnostic Skeletal Animal Reconstruction
Yuefan Wu, Zeyuan Chen, Shaowei Liu, Zhongzheng Ren, Shenlong Wang Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser Causality-Driven Hierarchical Structure Discovery for Reinforcement Learning
Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Eldar D Abraham, Karel D'Oosterlinck, Amir Feder, Yair Gat, Atticus Geiger, Christopher Potts, Roi Reichart, Zhengxuan Wu CEDe: A Collection of Expert-Curated Datasets with Atom-Level Entity Annotations for Optical Chemical Structure Recognition
Rodrigo Hormazabal, Changyoung Park, Soonyoung Lee, Sehui Han, Yeonsik Jo, Jaewan Lee, Ahra Jo, Seung Hwan Kim, Jaegul Choo, Moontae Lee, Honglak Lee Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou Challenging Common Assumptions in Convex Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Maribeth Rauh, John Mellor, Jonathan Uesato, Po-Sen Huang, Johannes Welbl, Laura Weidinger, Sumanth Dathathri, Amelia Glaese, Geoffrey Irving, Iason Gabriel, William Isaac, Lisa Anne Hendricks Characterizing Datapoints via Second-Split Forgetting
Pratyush Maini, Saurabh Garg, Zachary Lipton, J. Zico Kolter Chartalist: Labeled Graph Datasets for UTXO and Account-Based Blockchains
Kiarash Shamsi, Friedhelm Victor, Murat Kantarcioglu, Yulia Gel, Cuneyt G Akcora Chefs' Random Tables: Non-Trigonometric Random Features
Valerii Likhosherstov, Krzysztof M Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller Chromatic Correlation Clustering, Revisited
Qing Xiu, Kai Han, Jing Tang, Shuang Cui, He Huang Class-Aware Adversarial Transformers for Medical Image Segmentation
Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James Duncan Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization
De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
Tejas Srinivasan, Ting-Yun Chang, Leticia Pinto Alva, Georgios Chochlakis, Mohammad Rostami, Jesse Thomason Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Marina Danilova, David Dobre, Pavel Dvurechenskii, Alexander Gasnikov, Gauthier Gidel CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
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Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang Coarse-to-Fine Vision-Language Pre-Training with Fusion in the Backbone
Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, Jianfeng Gao, Lijuan Wang Coded Residual Transform for Generalizable Deep Metric Learning
Shichao Kan, Yixiong Liang, Min Li, Yigang Cen, Jianxin Wang, Zhihai He ComMU: Dataset for Combinatorial Music Generation
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Sam Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle Ecanow, Maxwell Nye, Michael Tessler, Josh Tenenbaum Composite Feature Selection Using Deep Ensembles
Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lió, Mateja Jamnik Conditional Meta-Learning of Linear Representations
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto Confident Adaptive Language Modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, Donald Metzler Conformal Off-Policy Prediction in Contextual Bandits
Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet Conformalized Fairness via Quantile Regression
Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang Constants of Motion Network
Muhammad Firmansyah Kasim, Yi Heng Lim Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan Sengupta, Dmitri B. Chklovskii Constrained Update Projection Approach to Safe Policy Optimization
Long Yang, Jiaming Ji, Juntao Dai, Linrui Zhang, Binbin Zhou, Pengfei Li, Yaodong Yang, Gang Pan CoNT: Contrastive Neural Text Generation
Chenxin An, Jiangtao Feng, Kai Lv, Lingpeng Kong, Xipeng Qiu, Xuanjing Huang Contact-Aware Human Motion Forecasting
Wei Mao, Miaomiao Liu, Richard I Hartley, Mathieu Salzmann Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard Turner Continual Learning in Environments with Polynomial Mixing Times
Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish Continual Learning with Evolving Class Ontologies
Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup Continuously Tempered PDMP Samplers
Matthew Sutton, Robert Salomone, Augustin Chevallier, Paul Fearnhead Contrastive Learning as Goal-Conditioned Reinforcement Learning
Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov Contrastive Neural Ratio Estimation
Benjamin K Miller, Christoph Weniger, Patrick Forré Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, Hongsheng Li Convexity Certificates from Hessians
Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen Coreset for Line-Sets Clustering
Sagi Lotan, Ernesto Evgeniy Sanches Shayda, Dan Feldman Coresets for Relational Data and the Applications
Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu R Could Giant Pre-Trained Image Models Extract Universal Representations?
Yutong Lin, Ze Liu, Zheng Zhang, Han Hu, Nanning Zheng, Stephen Lin, Yue Cao Counterfactual Fairness with Partially Known Causal Graph
Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong Counterfactual Harm
Jonathan Richens, Rory Beard, Daniel H. Thompson coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro CroCo: Self-Supervised Pre-Training for 3D Vision Tasks by Cross-View Completion
Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain Brégier, Yohann Cabon, Vaibhav Arora, Leonid Antsfeld, Boris Chidlovskii, Gabriela Csurka, Jerome Revaud Cross Aggregation Transformer for Image Restoration
Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan CUP: Critic-Guided Policy Reuse
Jin Zhang, Siyuan Li, Chongjie Zhang CyCLIP: Cyclic Contrastive Language-Image Pretraining
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan Rossi, Vishwa Vinay, Aditya Grover DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision
Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah Goodman DARE: Disentanglement-Augmented Rationale Extraction
Linan Yue, Qi Liu, Yichao Du, Yanqing An, Li Wang, Enhong Chen DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Daiheng Gao, Yuliang Xiu, Kailin Li, Lixin Yang, Feng Wang, Peng Zhang, Bang Zhang, Cewu Lu, Ping Tan Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James L. McClelland, Felix Hill Data-Driven Conditional Robust Optimization
Abhilash Reddy Chenreddy, Nymisha Bandi, Erick Delage Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Allen Nie, Yannis Flet-Berliac, Deon Jordan, William Steenbergen, Emma Brunskill DataMUX: Data Multiplexing for Neural Networks
Vishvak Murahari, Carlos Jimenez, Runzhe Yang, Karthik Narasimhan Dataset Distillation via Factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang Dataset Inference for Self-Supervised Models
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot DC-BENCH: Dataset Condensation Benchmark
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh DDXPlus: A New Dataset for Automatic Medical Diagnosis
Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, Joumana Ghosn Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Ya-Lin Zhang, Feng Zhu, Longfei Li, Jun Zhou, Linbo Jiang Debiased Self-Training for Semi-Supervised Learning
Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Aleksandr Beznosikov, Pavel Dvurechenskii, Anastasiia Koloskova, Valentin Samokhin, Sebastian U Stich, Alexander Gasnikov Decentralized Training of Foundation Models in Heterogeneous Environments
Binhang Yuan, Yongjun He, Jared Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang Decision Trees with Short Explainable Rules
Victor Feitosa Souza, Ferdinando Cicalese, Eduardo Laber, Marco Molinaro Decoupled Self-Supervised Learning for Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang Decoupling Classifier for Boosting Few-Shot Object Detection and Instance Segmentation
Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang, Congchong Nie, Jun Liu, Jinxiang Lai, Guannan Jiang, Xi Wang, Chengjie Wang Decoupling Knowledge from Memorization: Retrieval-Augmented Prompt Learning
Xiang Chen, Lei Li, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen Deep Active Learning by Leveraging Training Dynamics
Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew J Margenot, Jingrui He Deep Bidirectional Language-Knowledge Graph Pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy Liang, Jure Leskovec Deep Compression of Pre-Trained Transformer Models
Naigang Wang, Chi-Chun Liu, Swagath Venkataramani, Sanchari Sen, Chia-Yu Chen, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Leland Chang Deep Differentiable Logic Gate Networks
Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen Deep Ensembles Work, but Are They Necessary?
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham Deep Fourier Up-Sampling
Man Zhou, Hu Yu, Jie Huang, Feng Zhao, Jinwei Gu, Chen Change Loy, Deyu Meng, Chongyi Li Deep Generalized Schrödinger Bridge
Guan-Horng Liu, Tianrong Chen, Oswin So, Evangelos Theodorou Deep Hierarchical Planning from Pixels
Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Benjamin Kompa, David Bellamy, Tom Kolokotrones, James M Robins, Andrew Beam Deep Model Reassembly
Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, Xinchao Wang Deep Surrogate Assisted Generation of Environments
Varun Bhatt, Bryon Tjanaka, Matthew Fontaine, Stefanos Nikolaidis DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning
Yuko Ishiwaka, Xiao Zeng, Shun Ogawa, Donovan Westwater, Tadayuki Tone, Masaki Nakada DeepInteraction: 3D Object Detection via Modality Interaction
Zeyu Yang, Jiaqi Chen, Zhenwei Miao, Wei Li, Xiatian Zhu, Li Zhang Defining and Characterizing Reward Gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, David Krueger Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Henghui Ding, Yulun Zhang, Radu Timofte, Luc V Gool Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Lin Chen, Zhixiang Wei, Xin Jin, Huaian Chen, Miao Zheng, Kai Chen, Yi Jin Delving into Sequential Patches for Deepfake Detection
Jiazhi Guan, Hang Zhou, Zhibin Hong, Errui Ding, Jingdong Wang, Chengbin Quan, Youjian Zhao Denoising Diffusion Restoration Models
Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song Dense Interspecies Face Embedding
Sejong Yang, Subin Jeon, Seonghyeon Nam, Seon Joo Kim DENSE: Data-Free One-Shot Federated Learning
Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu Density-Driven Regularization for Out-of-Distribution Detection
Wenjian Huang, Hao Wang, Jiahao Xia, Chengyan Wang, Jianguo Zhang DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-Training for Open-World Detection
Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, Hang Xu DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen, Michalis Vazirgiannis Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings
Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour Differentiable Hierarchical and Surrogate Gradient Search for Spiking Neural Networks
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Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, Enayat Ullah Differentially Private Linear Sketches: Efficient Implementations and Applications
Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang Differentially Private Model Compression
FatemehSadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy Diffusion Models as Plug-and-Play Priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras Diffusion Visual Counterfactual Explanations
Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein Diffusion-LM Improves Controllable Text Generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto Direct Advantage Estimation
Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf Discovered Policy Optimisation
Chris Lu, Jakub Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster Discovery of Single Independent Latent Variable
Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex M Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes Disentangling Transfer in Continual Reinforcement Learning
Maciej Wolczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś Distinguishing Discrete and Continuous Behavioral Variability Using Warped Autoregressive HMMs
Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey Markowitz, Sandeep R Datta, Alex Williams, Scott Linderman Distributed Online Convex Optimization with Compressed Communication
Zhipeng Tu, Xi Wang, Yiguang Hong, Lei Wang, Deming Yuan, Guodong Shi Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal Distributionally Robust Weighted K-Nearest Neighbors
Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie DivBO: Diversity-Aware CASH for Ensemble Learning
Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin Cui Diverse Weight Averaging for Out-of-Distribution Generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, Matthieu Cord Divert More Attention to Vision-Language Tracking
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Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat Does Momentum Change the Implicit Regularization on Separable Data?
Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu Domain Adaptation Meets Individual Fairness. and They Get Along.
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun Domain Adaptation Under Open Set Label Shift
Saurabh Garg, Sivaraman Balakrishnan, Zachary Lipton DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland Doubly Robust Counterfactual Classification
Kwangho Kim, Edward Kennedy, Jose Zubizarreta DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu DropCov: A Simple yet Effective Method for Improving Deep Architectures
Qilong Wang, Mingze Gao, Zhaolin Zhang, Jiangtao Xie, Peihua Li, Qinghua Hu DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
Gang Li, Xiang Li, Yujie Wang, Wu Yichao, Ding Liang, Shanshan Zhang Dual-Discriminative Graph Neural Network for Imbalanced Graph-Level Anomaly Detection
Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng, Leman Akoglu, Charu Aggarwal Dungeons and Data: A Large-Scale NetHack Dataset
Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray Dynamic Fair Division with Partial Information
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Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu Dynamic Tensor Product Regression
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Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Wang, Mu Li, Dit-Yan Yeung EcoFormer: Energy-Saving Attention with Linear Complexity
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Chaofei Wang, Qisen Yang, Rui Huang, Shiji Song, Gao Huang Efficient Multi-Agent Communication via Self-Supervised Information Aggregation
Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, Yang Yu Efficient Risk-Averse Reinforcement Learning
Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor Efficient Training of Low-Curvature Neural Networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret EfficientFormer: Vision Transformers at MobileNet Speed
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Kevin Qinghong Lin, Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Z. Xu, Difei Gao, Rong-Cheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Wang HongFa, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
Gyubok Lee, Hyeonji Hwang, Seongsu Bae, Yeonsu Kwon, Woncheol Shin, Seongjun Yang, Minjoon Seo, Jong-Yeup Kim, Edward Choi ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
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Nilesh Gupta, Patrick Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon Eliciting Thinking Hierarchy Without a Prior
Yuqing Kong, Yunqi Li, Yubo Zhang, Zhihuan Huang, Jinzhao Wu ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward
Zixian Ma, Rose Wang, Fei-Fei Li, Michael Bernstein, Ranjay Krishna Embodied Scene-Aware Human Pose Estimation
Zhengyi Luo, Shun Iwase, Ye Yuan, Kris Kitani Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub Emergent Graphical Conventions in a Visual Communication Game
Shuwen Qiu, Sirui Xie, Lifeng Fan, Tao Gao, Jungseock Joo, Song-Chun Zhu, Yixin Zhu End-to-End Algorithm Synthesis with Recurrent Networks: Extrapolation Without Overthinking
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein End-to-End Stochastic Optimization with Energy-Based Model
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang End-to-End Symbolic Regression with Transformers
Pierre-alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, Francois Charton Enhance the Visual Representation via Discrete Adversarial Training
Xiaofeng Mao, YueFeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, Shaokai Ye, Xiaodan Li, Rong Zhang, Hui Xue' Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai Enhancing Safe Exploration Using Safety State Augmentation
Aivar Sootla, Alexander Cowen-Rivers, Jun Wang, Haitham Bou Ammar ENS-10: A Dataset for Post-Processing Ensemble Weather Forecasts
Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Zhenhong Sun, Ce Ge, Junyan Wang, Ming Lin, Hesen Chen, Hao Li, Xiuyu Sun EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, Shuicheng Yan Envy-Free Policy Teaching to Multiple Agents
Jiarui Gan, R Majumdar, Adish Singla, Goran Radanovic EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, Sanja Fidler, David Fouhey, Dima Damen EpiGRAF: Rethinking Training of 3D GANs
Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, Peter Wonka Equivariant Networks for Zero-Shot Coordination
Darius Muglich, Christian Schroeder de Witt, Elise van der Pol, Shimon Whiteson, Jakob Foerster Error Analysis of Tensor-Train Cross Approximation
Zhen Qin, Alexander Lidiak, Zhexuan Gong, Gongguo Tang, Michael B Wakin, Zhihui Zhu Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman Exact Shape Correspondence via 2D Graph Convolution
Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Ma Kaili, Bo Han, Bo Li, James Cheng Expected Improvement for Contextual Bandits
Hung Tran-The, Sunil Gupta, Santu Rana, Tuan Truong, Long Tran-Thanh, Svetha Venkatesh Expediting Large-Scale Vision Transformer for Dense Prediction Without Fine-Tuning
Weicong Liang, Yuhui Yuan, Henghui Ding, Xiao Luo, Weihong Lin, Ding Jia, Zheng Zhang, Chao Zhang, Han Hu Explainability via Causal Self-Talk
Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz Explainable Reinforcement Learning via Model Transforms
Mira Finkelstein, Nitsan Levy, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S Rosenschein, Sarah Keren Explaining Preferences with Shapley Values
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic Explicable Policy Search
Ze Gong, Yu ("Tony") Zhang Exploration via Elliptical Episodic Bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel Exploration via Planning for Information About the Optimal Trajectory
Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark Boyer, Stefano Ermon, Jeff G. Schneider, Willie Neiswanger Exploring Evolution-Aware & -free Protein Language Models as Protein Function Predictors
Mingyang Hu, Fajie Yuan, Kevin Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding Exploring Example Influence in Continual Learning
Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan Exploring Length Generalization in Large Language Models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro Exploring the Whole Rashomon Set of Sparse Decision Trees
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Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber Exponential Family Model-Based Reinforcement Learning via Score Matching
Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner FACT: Learning Governing Abstractions Behind Integer Sequences
Peter Belcak, Ard Kastrati, Flavio Schenker, Roger Wattenhofer Factuality Enhanced Language Models for Open-Ended Text Generation
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale N Fung, Mohammad Shoeybi, Bryan Catanzaro Fair Rank Aggregation
Diptarka Chakraborty, Syamantak Das, Arindam Khan, Aditya Subramanian Fair Wrapping for Black-Box Predictions
Alexander Soen, Ibrahim M Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie Fairness in Federated Learning via Core-Stability
Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta Fairness Reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Hao Liao, Zhongliang Yang, Yongfeng Huang, Xing Xie Falsification Before Extrapolation in Causal Effect Estimation
Zeshan M Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A Osborne Fast Distance Oracles for Any Symmetric Norm
Yichuan Deng, Zhao Song, Omri Weinstein, Ruizhe Zhang Fast Neural Kernel Embeddings for General Activations
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Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin Fault-Aware Neural Code Rankers
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Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Yanghe Feng, Guihai Chen FeLMi : Few Shot Learning with Hard Mixup
Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, Rama Chellappa FETA: Towards Specializing Foundational Models for Expert Task Applications
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Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman Few-Shot Fast-Adaptive Anomaly Detection
Ze Wang, Yipin Zhou, Rui Wang, Tsung-Yu Lin, Ashish Shah, Ser Nam Lim Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung Few-Shot Parameter-Efficient Fine-Tuning Is Better and Cheaper than In-Context Learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin A Raffel Few-Shot Task-Agnostic Neural Architecture Search for Distilling Large Language Models
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Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Wegner, Konrad Schindler FiLM: Frequency Improved Legendre Memory Model for Long-Term Time Series Forecasting
Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin Finding Naturally Occurring Physical Backdoors in Image Datasets
Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Zhao Fine-Grained Semantically Aligned Vision-Language Pre-Training
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Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang Fine-Tuning Language Models to Find Agreement Among Humans with Diverse Preferences
Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, Christopher Summerfield FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
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Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep K. Ravikumar First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux Flamingo: A Visual Language Model for Few-Shot Learning
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Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy Flexible Diffusion Modeling of Long Videos
William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood Flexible Neural Image Compression via Code Editing
Chenjian Gao, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin FlowHMM: Flow-Based Continuous Hidden Markov Models
Pawel Lorek, Rafal Nowak, Tomasz Trzcinski, Maciej Zieba Flowification: Everything Is a Normalizing Flow
Bálint Máté, Samuel Klein, Tobias Golling, François Fleuret FNeVR: Neural Volume Rendering for Face Animation
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Jianwei Yang, Chunyuan Li, Xiyang Dai, Jianfeng Gao Forecasting Future World Events with Neural Networks
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks Forecasting Human Trajectory from Scene History
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Tan Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley Osher, Nhat Ho FourierNets Enable the Design of Highly Non-Local Optical Encoders for Computational Imaging
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Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu GAMA: Generative Adversarial Multi-Object Scene Attacks
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Chien Lu, Jaakko Peltonen GBA: A Tuning-Free Approach to Switch Between Synchronous and Asynchronous Training for Recommendation Models
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Mingtian Zhang, Peter Hayes, David Barber Generalizing Bayesian Optimization with Decision-Theoretic Entropies
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon Generating Long Videos of Dynamic Scenes
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Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès Giving Feedback on Interactive Student Programs with Meta-Exploration
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David Buterez, Jon Paul Janet, Steven J Kiddle, Dino Oglic, Pietro Liò Graph Reordering for Cache-Efficient near Neighbor Search
Benjamin Coleman, Santiago Segarra, Alexander J Smola, Anshumali Shrivastava GRASP: Navigating Retrosynthetic Planning with Goal-Driven Policy
Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu GREED: A Neural Framework for Learning Graph Distance Functions
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Zeeshan Khan, C.V. Jawahar, Makarand Tapaswi Grounding Aleatoric Uncertainty for Unsupervised Environment Design
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Sanghyun Hong, Nicholas Carlini, Alexey Kurakin HAPI: A Large-Scale Longitudinal Dataset of Commercial ML API Predictions
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Haoyu Chen, Linqi Song, Zhenxing Qian, Xinpeng Zhang, Kede Ma Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
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Qihua Zhou, Song Guo, Yi Liu, Jie Zhang, Jiewei Zhang, Tao Guo, Zhenda Xu, Xun Liu, Zhihao Qu Hilbert Distillation for Cross-Dimensionality Networks
Dian Qin, Haishuai Wang, Zhe Liu, Hongjia Xu, Sheng Zhou, Jiajun Bu Homomorphic Matrix Completion
Xiao-Yang Liu, Zechu Li, Xiaodong Wang Honor of Kings Arena: An Environment for Generalization in Competitive Reinforcement Learning
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Theodore Sumers, Robert Hawkins, Mark K Ho, Tom Griffiths, Dylan Hadfield-Menell How Transferable Are Video Representations Based on Synthetic Data?
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Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David A. Forsyth, Jacob Steinhardt, Dan Hendrycks Hub-Pathway: Transfer Learning from a Hub of Pre-Trained Models
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Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou HUMANISE: Language-Conditioned Human Motion Generation in 3D Scenes
Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Yi.shi Xu, Dongsheng Wang, Bo Chen, Ruiying Lu, Zhibin Duan, Mingyuan Zhou HyperTree Proof Search for Neural Theorem Proving
Guillaume Lample, Timothee Lacroix, Marie-Anne Lachaux, Aurelien Rodriguez, Amaury Hayat, Thibaut Lavril, Gabriel Ebner, Xavier Martinet If Influence Functions Are the Answer, Then What Is the Question?
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B Grosse IKEA-Manual: Seeing Shape Assembly Step by Step
Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Ran Zhang, Chin-Yi Cheng, Jiajun Wu IM-Loss: Information Maximization Loss for Spiking Neural Networks
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Christos Thrampoulidis, Ganesh Ramachandra Kini, Vala Vakilian, Tina Behnia Imitating past Successes Can Be Very Suboptimal
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Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He Improved Coresets for Euclidean $k$-Means
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Yudong Chen, Sen Wang, Jiajun Liu, Xuwei Xu, Frank de Hoog, Zi Huang Improved Fine-Tuning by Better Leveraging Pre-Training Data
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Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani LasUIE: Unifying Information Extraction with Latent Adaptive Structure-Aware Generative Language Model
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Geng Yuan, Yanyu Li, Sheng Li, Zhenglun Kong, Sergey Tulyakov, Xulong Tang, Yanzhi Wang, Jian Ren Lazy and Fast Greedy MAP Inference for Determinantal Point Process
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Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu, Wenbing Huang, Thomas Li, Mingkui Tan, Chuang Gan Learning Best Combination for Efficient N:M Sparsity
Yuxin Zhang, Mingbao Lin, ZhiHang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng Learning Chaotic Dynamics in Dissipative Systems
Zongyi Li, Miguel Liu-Schiaffini, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar Learning Concept Credible Models for Mitigating Shortcuts
Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael Sjoding, Jenna Wiens Learning Contrastive Embedding in Low-Dimensional Space
Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama Learning Debiased Classifier with Biased Committee
Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, Suha Kwak Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Zhiwei Hao, Jianyuan Guo, Ding Jia, Kai Han, Yehui Tang, Chao Zhang, Han Hu, Yunhe Wang Learning Energy Networks with Generalized Fenchel-Young Losses
Mathieu Blondel, Felipe Llinares-Lopez, Robert Dadashi, Leonard Hussenot, Matthieu Geist Learning Enhanced Representation for Tabular Data via Neighborhood Propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David P Wipf Learning from a Sample in Online Algorithms
C.J. Argue, Alan Frieze, Anupam Gupta, Christopher Seiler Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie Liu Learning General World Models in a Handful of Reward-Free Deployments
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip Ball, Oleh Rybkin, S Roberts, Tim Rocktäschel, Edward Grefenstette Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee Learning in Congestion Games with Bandit Feedback
Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S Du Learning Interface Conditions in Domain Decomposition Solvers
Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott MacLachlan, Luke Olson, Matthew West Learning Latent Seasonal-Trend Representations for Time Series Forecasting
Zhiyuan Wang, Xovee Xu, Weifeng Zhang, Goce Trajcevski, Ting Zhong, Fan Zhou Learning Long-Term Crop Management Strategies with CyclesGym
Matteo Turchetta, Luca Corinzia, Scott Sussex, Amanda Burton, Juan Herrera, Ioannis Athanasiadis, Joachim M Buhmann, Andreas Krause Learning Low-Dimensional Generalizable Natural Features from Retina Using a U-Net
Siwei Wang, Benjamin Hoshal, Elizabeth de Laittre, Olivier Marre, Michael J. Berry Ii, Stephanie Palmer Learning Neural Acoustic Fields
Andrew Luo, Yilun Du, Michael Tarr, Josh Tenenbaum, Antonio Torralba, Chuang Gan Learning Neural Set Functions Under the Optimal Subset Oracle
Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian Learning on Arbitrary Graph Topologies via Predictive Coding
Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Tianyi Bao, Rafal Bogacz, Thomas Lukasiewicz Learning on the Edge: Online Learning with Stochastic Feedback Graphs
Emmanuel Esposito, Federico Fusco, Dirk van der Hoeven, Nicolò Cesa-Bianchi Learning Optical Flow from Continuous Spike Streams
Rui Zhao, Ruiqin Xiong, Jing Zhao, Zhaofei Yu, Xiaopeng Fan, Tiejun Huang Learning Options via Compression
Yiding Jiang, Evan Liu, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn Learning Physical Dynamics with Subequivariant Graph Neural Networks
Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Josh Tenenbaum, Chuang Gan Learning Physics Constrained Dynamics Using Autoencoders
Tsung-Yen Yang, Justinian P. Rosca, Karthik Narasimhan, Peter J Ramadge Learning Predictions for Algorithms with Predictions
Misha Khodak, Maria-Florina F Balcan, Ameet Talwalkar, Sergei Vassilvitskii Learning Robust Dynamics Through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou Learning Single-Index Models with Shallow Neural Networks
Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song Learning Sparse Features Can Lead to Overfitting in Neural Networks
Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart Learning Symmetric Rules with SATNet
Sangho Lim, Eun-Gyeol Oh, Hongseok Yang Learning to Branch with Tree MDPs
Lara Scavuzzo, Feng Chen, Didier Chetelat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen Aardal Learning to Constrain Policy Optimization with Virtual Trust Region
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Djordje Miladinovic, Kumar Shridhar, Kushal Jain, Max Paulus, Joachim M Buhmann, Carl Allen Learning to Follow Instructions in Text-Based Games
Mathieu Tuli, Andrew Li, Pashootan Vaezipoor, Toryn Klassen, Scott Sanner, Sheila McIlraith Learning to Mitigate AI Collusion on Economic Platforms
Gianluca Brero, Eric Mibuari, Nicolas Lepore, David C. Parkes Learning to Navigate Wikipedia by Taking Random Walks
Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon M. Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang Learning to Sample and Aggregate: Few-Shot Reasoning over Temporal Knowledge Graphs
Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek Abdelzaher Learning to Scaffold: Optimizing Model Explanations for Teaching
Patrick Fernandes, Marcos Treviso, Danish Pruthi, André Martins, Graham Neubig Learning with Little Mixing
Ingvar Ziemann, Stephen Tu LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Daejin Jo, Sungwoong Kim, Daniel Nam, Taehwan Kwon, Seungeun Rho, Jongmin Kim, Donghoon Lee Less-Forgetting Multi-Lingual Fine-Tuning
Yuren Mao, Yaobo Liang, Nan Duan, Haobo Wang, Kai Wang, Lu Chen, Yunjun Gao Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis
Xu Yan, Heshen Zhan, Chaoda Zheng, Jiantao Gao, Ruimao Zhang, Shuguang Cui, Zhen Li Lethal Dose Conjecture on Data Poisoning
Wenxiao Wang, Alexander Levine, Soheil Feizi LieGG: Studying Learned Lie Group Generators
Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold Smeulders LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks
Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee Linear Label Ranking with Bounded Noise
Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos Linear Tree Shap
Peng Yu, Albert Bifet, Jesse Read, Chao Xu LION: Latent Point Diffusion Models for 3D Shape Generation
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis LIPS - Learning Industrial Physical Simulation Benchmark Suite
Milad LEYLI Abadi, Antoine Marot, Jérôme Picault, David Danan, Mouadh Yagoubi, Benjamin Donnot, Seif Attoui, Pavel Dimitrov, Asma Farjallah, Clement Etienam Lipschitz Bandits with Batched Feedback
Yasong Feng, Zengfeng Huang, Tianyu Wang LISA: Learning Interpretable Skill Abstractions from Language
Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas Listen to Interpret: Post-Hoc Interpretability for Audio Networks with NMF
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard LiteTransformerSearch: Training-Free Neural Architecture Search for Efficient Language Models
Mojan Javaheripi, Gustavo de Rosa, Subhabrata Mukherjee, Shital Shah, Tomasz Religa, Caio Cesar Teodoro Mendes, Sebastien Bubeck, Farinaz Koushanfar, Debadeepta Dey Local Identifiability of Deep ReLU Neural Networks: The Theory
Joachim Bona-Pellissier, François Malgouyres, Francois Bachoc Local Latent Space Bayesian Optimization over Structured Inputs
Natalie Maus, Haydn Jones, Juston Moore, Matt J Kusner, John Bradshaw, Jacob Gardner Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim Local-Global MCMC Kernels: The Best of Both Worlds
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines Locating and Editing Factual Associations in GPT
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov Logical Credal Networks
Radu Marinescu, Haifeng Qian, Alexander G. Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel, Pravinda Sahu Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini Look More but Care Less in Video Recognition
Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Mansheej Paul, Brett Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite Luckiness in Multiscale Online Learning
Wouter M. Koolen, Muriel F. Pérez-Ortiz M$^4$I: Multi-Modal Models Membership Inference
Pingyi Hu, Zihan Wang, Ruoxi Sun, Hu Wang, Minhui Xue M2N: Mesh Movement Networks for PDE Solvers
Wenbin Song, Mingrui Zhang, Joseph G Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-Task Learning with Model-Accelerator Co-Design
Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus
Lichao Zhang, Ruiqi Li, Shoutong Wang, Liqun Deng, Jinglin Liu, Yi Ren, Jinzheng He, Rongjie Huang, Jieming Zhu, Xiao Chen, Zhou Zhao MABSplit: Faster Forest Training Using Multi-Armed Bandits
Mo Tiwari, Ryan Kang, Jaeyong Lee, Chris Piech, Ilan Shomorony, Sebastian Thrun, Martin J Zhang MAgNet: Mesh Agnostic Neural PDE Solver
Oussama Boussif, Yoshua Bengio, Loubna Benabbou, Dan Assouline Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach
Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, Dacheng Tao Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Pau de Jorge Aranda, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip Torr, Gregory Rogez, Puneet Dokania Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy Markovian Interference in Experiments
Vivek Farias, Andrew Li, Tianyi Peng, Andrew Zheng Mask Matching Transformer for Few-Shot Segmentation
Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi Masked Autoencoders as Spatiotemporal Learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He Masked Autoencoders That Listen
Po-Yao Huang, Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer Masked Generative Adversarial Networks Are Data-Efficient Generation Learners
Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric P. Xing Masked Prediction: A Parameter Identifiability View
Bingbin Liu, Daniel J. Hsu, Pradeep K. Ravikumar, Andrej Risteski MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi Amiri, Ghassan Hamarneh Matryoshka Representation Learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi Maximum Class Separation as Inductive Bias in One Matrix
Tejaswi Kasarla, Gertjan Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes Maximum Likelihood Training of Implicit Nonlinear Diffusion Model
Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-chul Moon MBW: Multi-View Bootstrapping in the Wild
Mosam Dabhi, Chaoyang Wang, Tim Clifford, László Jeni, Ian Fasel, Simon Lucey MCMAE: Masked Convolution Meets Masked Autoencoders
Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao Measuring Data Reconstruction Defenses in Collaborative Inference Systems
Mengda Yang, Ziang Li, Juan Wang, Hongxin Hu, Ao Ren, Xiaoyang Xu, Wenzhe Yi Memory Efficient Continual Learning with Transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cedric Archambeau Memory Safe Computations with XLA Compiler
Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk Mesoscopic Modeling of Hidden Spiking Neurons
Shuqi Wang, Valentin Schmutz, Guillaume Bellec, Wulfram Gerstner Meta-Album: Multi-Domain Meta-Dataset for Few-Shot Image Classification
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Xiang Huang, Zhanhong Ye, Hongsheng Liu, Shi Ji, Zidong Wang, Kang Yang, Yang Li, Min Wang, Haotian Chu, Fan Yu, Bei Hua, Lei Chen, Bin Dong Meta-Learning with Self-Improving Momentum Target
Jihoon Tack, Jongjin Park, Hankook Lee, Jaeho Lee, Jinwoo Shin MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
Jiangmeng Li, Wenwen Qiang, Yanan Zhang, Wenyi Mo, Changwen Zheng, Bing Su, Hui Xiong METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, Yining Hua, Zichang Su, Zhiyang Teng, Jiageng Wu, Jie Yang MExMI: Pool-Based Active Model Extraction Crossover Membership Inference
Yaxin Xiao, Qingqing Ye, Haibo Hu, Huadi Zheng, Chengfang Fang, Jie Shi MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar Minimax Optimal Online Imitation Learning via Replay Estimation
Gokul Swamy, Nived Rajaraman, Matt Peng, Sanjiban Choudhury, J. A. Bagnell, Steven Z. Wu, Jiantao Jiao, Kannan Ramchandran Minimax Regret for Cascading Bandits
Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant Mining Multi-Label Samples from Single Positive Labels
Youngin Cho, Daejin Kim, Mohammad Azam Khan, Jaegul Choo Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell Mixture-of-Experts with Expert Choice Routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Zhifeng Chen, Quoc V Le, James Laudon MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, Matthew Hausknecht MoCoDA: Model-Based Counterfactual Data Augmentation
Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg Model Preserving Compression for Neural Networks
Jerry Chee, Megan Flynn (née Renz), Anil Damle, Christopher M De Sa Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev, Xavier Giró-i-Nieto, Damian Borth Model-Based Imitation Learning for Urban Driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton Model-Based Opponent Modeling
XiaoPeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, Zongqing Lu Modeling the Machine Learning Multiverse
Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence Models Out of Line: A Fourier Lens on Distribution Shift Robustness
Sara Fridovich-Keil, Brian Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer Moderate-Fitting as a Natural Backdoor Defender for Pre-Trained Language Models
Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, Ming Gu Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg Module-Aware Optimization for Auxiliary Learning
Hong Chen, Xin Wang, Yue Liu, Yuwei Zhou, Chaoyu Guan, Wenwu Zhu MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing
Zelun Luo, Zane Durante, Linden Li, Wanze Xie, Ruochen Liu, Emily Jin, Zhuoyi Huang, Lun Yu Li, Jiajun Wu, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li Moment Distributionally Robust Tree Structured Prediction
Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian Ziebart, Kevin Gimpel Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Kien Do, Thai Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh Monocular Dynamic View Synthesis: A Reality Check
Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, Angjoo Kanazawa MorphTE: Injecting Morphology in Tensorized Embeddings
Guobing Gan, Peng Zhang, Sunzhu Li, Xiuqing Lu, Benyou Wang MsSVT: Mixed-Scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds
Shaocong Dong, Lihe Ding, Haiyang Wang, Tingfa Xu, Xinli Xu, Jie Wang, Ziyang Bian, Ying Wang, Jianan Li MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William Gray-Roncal, Erik Johnson, Eva Dyer Multi-Agent Dynamic Algorithm Configuration
Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem
Muning Wen, Jakub Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang Multi-Class $h$-Consistency Bounds
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong Multi-Fidelity Best-Arm Identification
Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli Multi-Game Decision Transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, Igor Mordatch Multi-Layer State Evolution Under Random Convolutional Design
Mara Daniels, Cedric Gerbelot, Florent Krzakala, Lenka Zdeborová Multi-Objective Deep Data Generation with Correlated Property Control
Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Petersen, Austin Leitgeb, Saleh Alkhalifa, Kevin Minbiole, William M. Wuest, Amarda Shehu, Liang Zhao Multi-Sample Training for Neural Image Compression
Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin Multi-Scale Adaptive Network for Single Image Denoising
Yuanbiao Gou, Peng Hu, Jiancheng Lv, Joey Tianyi Zhou, Xi Peng Multi-View Subspace Clustering on Topological Manifold
Shudong Huang, Hongjie Wu, Yazhou Ren, Ivor W. Tsang, Zenglin Xu, Wentao Feng, Jiancheng Lv Multiagent Q-Learning with Sub-Team Coordination
Wenhan Huang, Kai Li, Kun Shao, Tianze Zhou, Matthew Taylor, Jun Luo, Dongge Wang, Hangyu Mao, Jianye Hao, Jun Wang, Xiaotie Deng Multilingual Abusive Comment Detection at Scale for Indic Languages
Vikram Gupta, Sumegh Roychowdhury, Mithun Das, Somnath Banerjee, Punyajoy Saha, Binny Mathew, Hastagiri Prakash Vanchinathan, Animesh Mukherjee Multimodal Contrastive Learning with LIMoE: The Language-Image Mixture of Experts
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, Wei Chen Multiview Human Body Reconstruction from Uncalibrated Cameras
Zhixuan Yu, Linguang Zhang, Yuanlu Xu, Chengcheng Tang, Luan Tran, Cem Keskin, Hyun Soo Park Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
Renbo Tu, Nicholas Roberts, Misha Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter Natural Color Fool: Towards Boosting Black-Box Unrestricted Attacks
Shengming Yuan, Qilong Zhang, Lianli Gao, Yaya Cheng, Jingkuan Song Natural Gradient Enables Fast Sampling in Spiking Neural Networks
Paul Masset, Jacob Zavatone-Veth, J. Patrick Connor, Venkatesh Murthy, Cengiz Pehlevan Near-Optimal Collaborative Learning in Bandits
Clémence Réda, Sattar Vakili, Emilie Kaufmann Near-Optimal Correlation Clustering with Privacy
Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub M Tarnawski Near-Optimal Multi-Agent Learning for Safe Coverage Control
Manish Prajapat, Matteo Turchetta, Melanie Zeilinger, Andreas Krause Near-Optimal No-Regret Learning Dynamics for General Convex Games
Gabriele Farina, Ioannis Anagnostides, Haipeng Luo, Chung-Wei Lee, Christian Kroer, Tuomas Sandholm Near-Optimal Private and Scalable $k$-Clustering
Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
Tiancheng Jin, Tal Lancewicki, Haipeng Luo, Yishay Mansour, Aviv Rosenberg Nearly-Tight Bounds for Testing Histogram Distributions
Clément L Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu NeMF: Neural Motion Fields for Kinematic Animation
Chengan He, Jun Saito, James Zachary, Holly Rushmeier, Yi Zhou NeoRL: A near Real-World Benchmark for Offline Reinforcement Learning
Rong-Jun Qin, Xingyuan Zhang, Songyi Gao, Xiong-Hui Chen, Zewen Li, Weinan Zhang, Yang Yu NeuForm: Adaptive Overfitting for Neural Shape Editing
Connor Lin, Niloy Mitra, Gordon Wetzstein, Leonidas Guibas, Paul Guerrero Neur2SP: Neural Two-Stage Stochastic Programming
Rahul Mihir Patel, Justin Dumouchelle, Elias Khalil, Merve Bodur Neural Abstractions
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Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen Neural Attentive Circuits
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Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lió, Michael Bronstein Neural Stochastic Control
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Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Non-Linear Coordination Graphs
Yipeng Kang, Tonghan Wang, Qianlan Yang, Xiaoran Wu, Chongjie Zhang Non-Stationary Bandits with Knapsacks
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Jian Liang, Chenfei Wu, Xiaowei Hu, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
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Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetic, Mario Lucic, Leonidas Guibas, Klaus Greff, Thomas Kipf Object-Category Aware Reinforcement Learning
Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Yash Chandak, Shiv Shankar, Nathaniel Bastian, Bruno da Silva, Emma Brunskill, Philip S. Thomas Off-Policy Evaluation with Deficient Support Using Side Information
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Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola Okapi: Generalising Better by Making Statistical Matches Match
Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto Old Can Be Gold: Better Gradient Flow Can Make Vanilla-GCNs Great Again
Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin Rousseau, Ying Ding, Zhangyang Wang OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
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Farnam Mansouri, Hans Simon, Adish Singla, Sandra Zilles On Computing Probabilistic Explanations for Decision Trees
Marcelo Arenas, Pablo Barceló, Miguel Romero Orth, Bernardo Subercaseaux On Divergence Measures for Bayesian Pseudocoresets
Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew G Wilson On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui, Gezheng Xu, Qi Chen, Jiaqi Li, Charles X. Ling, Tal Arbel, Boyu Wang, Christian Gagné On Leave-One-Out Conditional Mutual Information for Generalization
Mohamad Rida Rammal, Alessandro Achille, Aditya Golatkar, Suhas Diggavi, Stefano Soatto On Measuring Excess Capacity in Neural Networks
Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt On Non-Linear Operators for Geometric Deep Learning
Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon On Optimal Learning Under Targeted Data Poisoning
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli On the Complexity of Adversarial Decision Making
Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan On the Convergence of Policy Gradient Methods to Nash Equilibria in General Stochastic Games
Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos, Emmanouil-Vasileios Vlatakis-Gkaragkounis On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, Simone Calderara On the Effectiveness of Persistent Homology
Renata Turkes, Guido F. Montufar, Nina Otter On the Epistemic Limits of Personalized Prediction
Lucas Monteiro Paes, Carol Long, Berk Ustun, Flavio Calmon On the Frequency-Bias of Coordinate-MLPs
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Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White On the Generalization of Learning Algorithms That Do Not Converge
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka On the Representation Collapse of Sparse Mixture of Experts
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Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R Varshney, Elizabeth Daly, Moninder Singh On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
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Naoki Hiratani, Yash Mehta, Timothy Lillicrap, Peter E Latham On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal On the Theoretical Properties of Noise Correlation in Stochastic Optimization
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Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang Out-of-Distribution Detection via Conditional Kernel Independence Model
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Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani PAC-Bayes Compression Bounds so Tight That They Can Explain Generalization
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Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma PeRFception: Perception Using Radiance Fields
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Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos PerfectDou: Dominating DouDizhu with Perfect Information Distillation
Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, Yue Lin Performative Power
Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner Peripheral Vision Transformer
Juhong Min, Yucheng Zhao, Chong Luo, Minsu Cho Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi Physically-Based Face Rendering for NIR-VIS Face Recognition
Yunqi Miao, Alexandros Lattas, Jiankang Deng, Jungong Han, Stefanos Zafeiriou Physics-Informed Implicit Representations of Equilibrium Network Flows
Kevin D. Smith, Francesco Seccamonte, Ananthram Swami, Francesco Bullo Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
Peter Henderson, Mark Krass, Lucia Zheng, Neel Guha, Christopher D Manning, Dan Jurafsky, Daniel Ho Planning for Sample Efficient Imitation Learning
Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao Pluralistic Image Completion with Gaussian Mixture Models
Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu Point-M2AE: Multi-Scale Masked Autoencoders for Hierarchical Point Cloud Pre-Training
Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, Hongsheng Li PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, Bernard Ghanem Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao Polynomial Neural Fields for Subband Decomposition and Manipulation
Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie Post-Hoc Estimators for Learning to Defer to an Expert
Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya K Menon, Ankit Rawat, Sanjiv Kumar Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham Practical Adversarial Multivalid Conformal Prediction
Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G Wilson Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu Pre-Trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Leon Hetzel, Simon Boehm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis Predictive Coding Beyond Gaussian Distributions
Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, Thomas Lukasiewicz Private and Communication-Efficient Algorithms for Entropy Estimation
Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andres Munoz Medina, Umar Syed Private Estimation with Public Data
Alex Bie, Gautam Kamath, Vikrant Singhal Private Isotonic Regression
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi Private Synthetic Data for Multitask Learning and Marginal Queries
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Eric Chen, Zhang-Wei Hong, Joni K. Pajarinen, Pulkit Agrawal Redistribution of Weights and Activations for AdderNet Quantization
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Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc Bellemare Reinforcement Learning with a Terminator
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Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint Reinforcement Learning with Non-Exponential Discounting
Matthias Schultheis, Constantin A Rothkopf, Heinz Koeppl Repairing Neural Networks by Leaving the Right past Behind
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Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I Shamir Residual Multiplicative Filter Networks for Multiscale Reconstruction
Shayan Shekarforoush, David Lindell, David J Fleet, Marcus A Brubaker Retaining Knowledge for Learning with Dynamic Definition
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Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond Rethinking Image Restoration for Object Detection
Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao Rethinking Resolution in the Context of Efficient Video Recognition
Chuofan Ma, Qiushan Guo, Yi Jiang, Ping Luo, Zehuan Yuan, Xiaojuan Qi Rethinking the Reverse-Engineering of Trojan Triggers
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Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A Bilmes Revisiting Heterophily for Graph Neural Networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup Revisiting Neural Scaling Laws in Language and Vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai Revisiting Sparse Convolutional Model for Visual Recognition
Xili Dai, Mingyang Li, Pengyuan Zhai, Shengbang Tong, Xingjian Gao, Shao-Lun Huang, Zhihui Zhu, Chong You, Yi Ma Riemannian Diffusion Models
Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville Riemannian Score-Based Generative Modelling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet Risk-Driven Design of Perception Systems
Anthony Corso, Sydney Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J Kochenderfer RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic RLIP: Relational Language-Image Pre-Training for Human-Object Interaction Detection
Hangjie Yuan, Jianwen Jiang, Samuel Albanie, Tao Feng, Ziyuan Huang, Dong Ni, Mingqian Tang Robust $\phi$-Divergence MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann Robust Anytime Learning of Markov Decision Processes
Marnix Suilen, Thiago D. Simão, David B. Parker, Nils Jansen Robust Feature-Level Adversaries Are Interpretability Tools
Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman Robust Graph Structure Learning via Multiple Statistical Tests
Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin Robust Imitation via Mirror Descent Inverse Reinforcement Learning
Dong-Sig Han, Hyunseo Kim, Hyundo Lee, JeHwan Ryu, Byoung-Tak Zhang Robust Learning Against Relational Adversaries
Yizhen Wang, Mohannad Alhanahnah, Xiaozhu Meng, Ke Wang, Mihai Christodorescu, Somesh Jha Robust Models Are Less Over-Confident
Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper Robust Neural Posterior Estimation and Statistical Model Criticism
Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian Schmon Robust Reinforcement Learning Using Offline Data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh Robust Rent Division
Dominik Peters, Ariel D Procaccia, David Zhu Robust Semi-Supervised Learning When Not All Classes Have Labels
Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li Robust Streaming PCA
Daniel Bienstock, Minchan Jeong, Apurv Shukla, Se-Young Yun Robustness Disparities in Face Detection
Samuel Dooley, George Z Wei, Tom Goldstein, John Dickerson Robustness to Unbounded Smoothness of Generalized SignSGD
Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang Root Cause Analysis of Failures in Microservices Through Causal Discovery
Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Saini, Saurabh Bagchi, Murat Kocaoglu RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, Lei Han RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-Supervised Learning
Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang S3GC: Scalable Self-Supervised Graph Clustering
Fnu Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, Christopher Ré Safe Opponent-Exploitation Subgame Refinement
Mingyang Liu, Chengjie Wu, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, Chongjie Zhang SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li SageMix: Saliency-Guided Mixup for Point Clouds
Sanghyeok Lee, Minkyu Jeon, Injae Kim, Yunyang Xiong, Hyunwoo J Kim SALSA: Attacking Lattice Cryptography with Transformers
Emily Wenger, Mingjie Chen, Francois Charton, Kristin E. Lauter Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao Sample-Then-Optimize Batch Neural Thompson Sampling
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet SAMURAI: Shape and Material from Unconstrained Real-World Arbitrary Image Collections
Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan Barron, Hendrik PA Lensch, Varun Jampani SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
Hao Lu, Wenze Liu, Zixuan Ye, Hongtao Fu, Yuliang Liu, Zhiguo Cao SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training
Yangrui Chen, Cong Xie, Meng Ma, Juncheng Gu, Yanghua Peng, Haibin Lin, Chuan Wu, Yibo Zhu SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
Gamaleldin Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael Mozer, Thomas Kipf SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
Chuanyang Zheng, Zheyang Li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, Shiliang Pu Scalable Infomin Learning
Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller Scalable Interpretability via Polynomials
Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit SCAMPS: Synthetics for Camera Measurement of Physiological Signals
Daniel McDuff, Miah Wander, Xin Liu, Brian Hill, Javier Hernandez, Jonathan Lester, Tadas Baltrusaitis SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Xiyue Wang, Jinxi Xiang, Jun Zhang, Sen Yang, Zhongyi Yang, Ming-Hui Wang, Jing Zhang, Wei Yang, Junzhou Huang, Xiao Han Score-Based Diffusion Meets Annealed Importance Sampling
Arnaud Doucet, Will Grathwohl, Alexander G Matthews, Heiko Strathmann SecureFedYJ: A Safe Feature Gaussianization Protocol for Federated Learning
Tanguy Marchand, Boris Muzellec, Constance Béguier, Jean Ogier du Terrail, Mathieu Andreux SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, Shi-min Hu SegViT: Semantic Segmentation with Plain Vision Transformers
Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, Yifan Liu SelecMix: Debiased Learning by Contradicting-Pair Sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, Byoung-Tak Zhang Self-Aware Personalized Federated Learning
Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang Self-Explaining Deep Models with Logic Rule Reasoning
Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha Self-Explaining Deviations for Coordination
Hengyuan Hu, Samuel Sokota, David Wu, Anton Bakhtin, Andrei Lupu, Brandon Cui, Jakob Foerster Self-Organized Group for Cooperative Multi-Agent Reinforcement Learning
Jianzhun Shao, Zhiqiang Lou, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon Self-Supervised Amodal Video Object Segmentation
Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P Wipf, Yanwei Fu, Zheng Zhang Self-Supervised Learning with an Information Maximization Criterion
Serdar Ozsoy, Shadi Hamdan, Sercan Arik, Deniz Yuret, Alper Erdogan Semantic Exploration from Language Abstractions and Pretrained Representations
Allison Tam, Neil Rabinowitz, Andrew Lampinen, Nicholas A. Roy, Stephanie Chan, Dj Strouse, Jane Wang, Andrea Banino, Felix Hill Semantic Probabilistic Layers for Neuro-Symbolic Learning
Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari Semantic Uncertainty Intervals for Disentangled Latent Spaces
Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola Semi-Supervised Active Linear Regression
Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi Semi-Supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro, Manchen Wang, Davide Modolo, Rahul Bhotika, Zhuowen Tu, Stefano Soatto SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, Changwen Zheng SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, He He Sequence Model Imitation Learning with Unobserved Contexts
Gokul Swamy, Sanjiban Choudhury, J. A. Bagnell, Steven Z. Wu Sequence-to-Set Generative Models
Longtao Tang, Ying Zhou, Yu Yang Sequential Information Design: Learning to Persuade in the Dark
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò Set-Based Meta-Interpolation for Few-Task Meta-Learning
Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang Shape and Structure Preserving Differential Privacy
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Jiawei Du, Daquan Zhou, Jiashi Feng, Vincent Tan, Joey Tianyi Zhou Signal Processing for Implicit Neural Representations
Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, Zhangyang Wang Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse
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Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas Simulation-Guided Beam Search for Neural Combinatorial Optimization
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Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang Single Model Uncertainty Estimation via Stochastic Data Centering
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Yanpeng Sun, Qiang Chen, Xiangyu He, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jian Cheng, Zechao Li, Jingdong Wang SIXO: Smoothing Inference with Twisted Objectives
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Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang SKFlow: Learning Optical Flow with Super Kernels
Shangkun Sun, Yuanqi Chen, Yu Zhu, Guodong Guo, Ge Li Smoothed Embeddings for Certified Few-Shot Learning
Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg SnAKe: Bayesian Optimization with Pathwise Exploration
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Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun, Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang Society of Agents: Regret Bounds of Concurrent Thompson Sampling
Yan Chen, Perry Dong, Qinxun Bai, Maria Dimakopoulou, Wei Xu, Zhengyuan Zhou SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, Feng Zheng SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao Solving Quantitative Reasoning Problems with Language Models
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Elias Abad Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning
Changan Chen, Carl Schissler, Sanchit Garg, Philip Kobernik, Alexander Clegg, Paul Calamia, Dhruv Batra, Philip Robinson, Kristen Grauman SparCL: Sparse Continual Learning on the Edge
Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel Bruinsma, David Burt, Carl Edward Rasmussen Sparse Structure Search for Delta Tuning
Shengding Hu, Zhen Zhang, Ning Ding, Yadao Wang, Yasheng Wang, Zhiyuan Liu, Maosong Sun Sparse Winning Tickets Are Data-Efficient Image Recognizers
Mukund Varma T, Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang Sparsity in Continuous-Depth Neural Networks
Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian J. Theis, Niki Kilbertus Spatial Mixture-of-Experts
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Jianhui Liu, Yukang Chen, Xiaoqing Ye, Zhuotao Tian, Xiao Tan, Xiaojuan Qi Spherical Channels for Modeling Atomic Interactions
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Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer Stability and Generalization of Kernel Clustering: From Single Kernel to Multiple Kernel
Weixuan Liang, Xinwang Liu, Yong Liu, Sihang Zhou, Jun-Jie Huang, Siwei Wang, Jiyuan Liu, Yi Zhang, En Zhu Staircase Attention for Recurrent Processing of Sequences
Da Ju, Stephen Roller, Sainbayar Sukhbaatar, Jason E Weston STaR: Bootstrapping Reasoning with Reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah Goodman Stars: Tera-Scale Graph Building for Clustering and Learning
Cj Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong Stochastic Adaptive Activation Function
Kyungsu Lee, Jaeseung Yang, Haeyun Lee, Jae Youn Hwang Stochastic Multiple Target Sampling Gradient Descent
Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung Streaming Radiance Fields for 3D Video Synthesis
Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan StrokeRehab: A Benchmark Dataset for Sub-Second Action Identification
Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi Schambra, Carlos Fernandez-Granda Structural Knowledge Distillation for Object Detection
Philip de Rijk, Lukas Schneider, Marius Cordts, Dariu Gavrila Structural Pruning via Latency-Saliency Knapsack
Maying Shen, Hongxu Yin, Pavlo Molchanov, Lei Mao, Jianna Liu, Jose M. Alvarez Structure-Preserving 3D Garment Modeling with Neural Sewing Machines
Xipeng Chen, Guangrun Wang, Dizhong Zhu, Xiaodan Liang, Philip Torr, Liang Lin Structured Energy Network as a Loss
Jay Yoon Lee, Dhruvesh Patel, Purujit Goyal, Wenlong Zhao, Zhiyang Xu, Andrew McCallum Structuring Representations Using Group Invariants
Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh Subgame Solving in Adversarial Team Games
Brian Zhang, Luca Carminati, Federico Cacciamani, Gabriele Farina, Pierriccardo Olivieri, Nicola Gatti, Tuomas Sandholm Sublinear Algorithms for Hierarchical Clustering
Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil Submodular Maximization in Clean Linear Time
Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi Subsidiary Prototype Alignment for Universal Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay R Kulkarni, Hiran Sarkar, Varun Jampani, Venkatesh Babu R Supervised Training of Conditional Monge Maps
Charlotte Bunne, Andreas Krause, Marco Cuturi Surprise Minimizing Multi-Agent Learning with Energy-Based Models
Karush Suri, Xiao Qi Shi, Konstantinos N Plataniotis, Yuri Lawryshyn Sustainable Online Reinforcement Learning for Auto-Bidding
Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng Symbolic Distillation for Learned TCP Congestion Control
S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang Symmetry-Induced Disentanglement on Graphs
Giangiacomo Mercatali, Andre Freitas, Vikas Garg Synergy-of-Experts: Collaborate to Improve Adversarial Robustness
Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang TA-GATES: An Encoding Scheme for Neural Network Architectures
Xuefei Ning, Zixuan Zhou, Junbo Zhao, Tianchen Zhao, Yiping Deng, Changcheng Tang, Shuang Liang, Huazhong Yang, Yu Wang TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V Le, Da Huang TaiSu: A 166m Large-Scale High-Quality Dataset for Chinese Vision-Language Pre-Training
Yulong Liu, Guibo Zhu, Bin Zhu, Qi Song, Guojing Ge, Haoran Chen, GuanHui Qiao, Ru Peng, Lingxiang Wu, Jinqiao Wang TAP-Vid: A Benchmark for Tracking Any Point in a Video
Carl Doersch, Ankush Gupta, Larisa Markeeva, Adria Recasens, Lucas Smaira, Yusuf Aytar, Joao Carreira, Andrew Zisserman, Yi Yang TaSIL: Taylor Series Imitation Learning
Daniel Pfrommer, Thomas Zhang, Stephen Tu, Nikolai Matni Task Discovery: Finding the Tasks That Neural Networks Generalize on
Andrei Atanov, Andrei Filatov, Teresa Yeo, Ajay Sohmshetty, Amir Zamir Task-Agnostic Graph Explanations
Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji TCT: Convexifying Federated Learning Using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation
Yichen Zhu, Ning Liu, Zhiyuan Xu, Xin Liu, Weibin Meng, Louis Wang, Zhicai Ou, Jian Tang TempEL: Linking Dynamically Evolving and Newly Emerging Entities
Klim Zaporojets, Lucie-Aimée Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, Isabelle Augenstein Template Based Graph Neural Network with Optimal Transport Distances
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty Temporal Effective Batch Normalization in Spiking Neural Networks
Chaoteng Duan, Jianhao Ding, Shiyan Chen, Zhaofei Yu, Tiejun Huang Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh Bharadwaj Gundavarapu, Alex M Lamb, Nan Rosemary Ke, Yoshua Bengio Tenrec: A Large-Scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, Xiaohu Qie Tensor Program Optimization with Probabilistic Programs
Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen Tensor Wheel Decomposition and Its Tensor Completion Application
Zhong-Cheng Wu, Ting-Zhu Huang, Liang-Jian Deng, Hong-Xia Dou, Deyu Meng Test Time Adaptation via Conjugate Pseudo-Labels
Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao Test-Time Training with Masked Autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei Efros Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
Chengzhi Lin, Ancong Wu, Junwei Liang, Jun Zhang, Wenhang Ge, Wei-Shi Zheng, Chunhua Shen The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
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Afonso S Bandeira, Ahmed El Alaoui, Samuel Hopkins, Tselil Schramm, Alexander S Wein, Ilias Zadik The Hessian Screening Rule
Johan Larsson, Jonas Wallin The Implicit Delta Method
Nathan Kallus, James McInerney The Least-Control Principle for Local Learning at Equilibrium
Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento The Neural Testbed: Evaluating Joint Predictions
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Tom Schaul, Andre Barreto, John Quan, Georg Ostrovski The Privacy Onion Effect: Memorization Is Relative
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Peter Kocsis, Peter Súkeník, Guillem Braso, Matthias Niessner, Laura Leal-Taixé, Ismail Elezi Theseus: A Library for Differentiable Nonlinear Optimization
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Qing Guo, Junya Chen, Dong Wang, Yuewei Yang, Xinwei Deng, Jing Huang, Larry Carin, Fan Li, Chenyang Tao ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
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Pengfei Li, Beiwen Tian, Yongliang Shi, Xiaoxue Chen, Hao Zhao, Guyue Zhou, Ya-Qin Zhang Top Two Algorithms Revisited
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