NeurIPS 2021
2334 papers
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
Le Hui, Lingpeng Wang, Mingmei Cheng, Jin Xie, Jian Yang 3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles A. Sutton A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, Xiaokang Yang A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio A Continuous Mapping for Augmentation Design
Keyu Tian, Chen Lin, Ser Nam Lim, Wanli Ouyang, Puneet Dokania, Philip Torr A Flow-Based Latent State Generative Model of Neural Population Responses to Natural Images
Mohammad Bashiri, Edgar Walker, Konstantin-Klemens Lurz, Akshay Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas Tolias, Fabian H. Sinz A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc A Gang of Adversarial Bandits
Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu A Hierarchical Reinforcement Learning Based Optimization Framework for Large-Scale Dynamic Pickup and Delivery Problems
Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng A Multi-Implicit Neural Representation for Fonts
Pradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher, Hailin Jin, Niloy Mitra A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang A PAC-Bayes Analysis of Adversarial Robustness
Paul Viallard, Eric Guillaume Vidot, Amaury Habrard, Emilie Morvant A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou A Separation Result Between Data-Oblivious and Data-Aware Poisoning Attacks
Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta A Stochastic Newton Algorithm for Distributed Convex Optimization
Brian Bullins, Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E Woodworth A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness
James Diffenderfer, Brian Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura A/B Testing for Recommender Systems in a Two-Sided Marketplace
Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee A/B/n Testing with Control in the Presence of Subpopulations
Yoan Russac, Christina Katsimerou, Dennis Bohle, Olivier Cappé, Aurélien Garivier, Wouter M. Koolen Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E Gonzalez, Ion Stoica, Ken Goldberg Accumulative Poisoning Attacks on Real-Time Data
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu Accurate Point Cloud Registration with Robust Optimal Transport
Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel H Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer Accurately Solving Rod Dynamics with Graph Learning
Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Soeren Pirk, Dominik L Michels Action-Guided 3D Human Motion Prediction
Jiangxin Sun, Zihang Lin, Xintong Han, Jian-Fang Hu, Jia Xu, Wei-Shi Zheng Active 3D Shape Reconstruction from Vision and Touch
Edward Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero Soriano, Michal Drozdzal Active Clustering for Labeling Training Data
Quentin Lutz, Elie de Panafieu, Maya Stein, Alex Scott Active Offline Policy Selection
Ksenia Konyushova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas Adaptive Data Augmentation on Temporal Graphs
Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi Adaptive Denoising via GainTuning
Sreyas Mohan, Joshua L Vincent, Ramon Manzorro, Peter Crozier, Carlos Fernandez-Granda, Eero P. Simoncelli Adaptive Diffusion in Graph Neural Networks
Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites Adaptive Online Packing-Guided Search for POMDPs
Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, Jianye Hao Adaptive Risk Minimization: Learning to Adapt to Domain Shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn Adaptive Sampling for Minimax Fair Classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi Adaptive Wavelet Distillation from Neural Networks Through Interpretations
Wooseok Ha, Chandan Singh, Francois Lanusse, Srigokul Upadhyayula, Bin Yu Adder Attention for Vision Transformer
Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu, Salman Asif, Chengyu Song, Amit Roy-Chowdhury, Srikanth Krishnamurthy Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A Osborne, Xiaowen Dong Adversarial Examples in Multi-Layer Random ReLU Networks
Peter L. Bartlett, Sebastien Bubeck, Yeshwanth Cherapanamjeri Adversarial Examples Make Strong Poisons
Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein Adversarial Feature Desensitization
Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Richards, Irina Rish Adversarial Regression with Doubly Non-Negative Weighting Matrices
Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen Adversarial Robustness of Streaming Algorithms Through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou Adversarial Robustness with Non-Uniform Perturbations
Ecenaz Erdemir, Jeffrey Bickford, Luca Melis, Sergul Aydore Adversarial Robustness with Semi-Infinite Constrained Learning
Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro Adversarial Training Helps Transfer Learning via Better Representations
Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y Zou Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Jiachen Sun, Yulong Cao, Christopher B Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao Adversarially Robust Learning for Security-Constrained Optimal Power Flow
Priya Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry Pileggi, J. Zico Kolter AFEC: Active Forgetting of Negative Transfer in Continual Learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, Steven Chu Hong Hoi Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Ramesha Rakesh Mugaludi, Jogendra Nath Kundu, Varun Jampani, Venkatesh Babu R Alignment Attention by Matching Key and Query Distributions
Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, Mingyuan Zhou All Tokens Matter: Token Labeling for Training Better Vision Transformers
Zi-Hang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang, Weixun Wang, Hongyao Tang, Jianye Hao, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yingfeng Chen, Yujing Hu, Changjie Fan, Chengwei Zhang An Uncertainty Principle Is a Price of Privacy-Preserving Microdata
John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong Liu Anti-Backdoor Learning: Training Clean Models on Poisoned Data
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramer Approximate Optimization of Convex Functions with Outlier Noise
Anindya De, Sanjeev Khanna, Huan Li, MohammadHesam NikpeySalekde Are My Deep Learning Systems Fair? an Empirical Study of Fixed-Seed Training
Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah Are Transformers More Robust than CNNs?
Yutong Bai, Jieru Mei, Alan L. Yuille, Cihang Xie Argmax Centroid
Chengyue Gong, Mao Ye, Qiang Liu Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling Artistic Style Transfer with Internal-External Learning and Contrastive Learning
Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu Associative Memories via Predictive Coding
Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz Asynchronous Decentralized Online Learning
Jiyan Jiang, Wenpeng Zhang, Jinjie Gu, Wenwu Zhu Asynchronous Decentralized SGD with Quantized and Local Updates
Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, Sanja Fidler Attention Bottlenecks for Multimodal Fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun AugMax: Adversarial Composition of Random Augmentations for Robust Training
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang Augmented Shortcuts for Vision Transformers
Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang AutoBalance: Optimized Loss Functions for Imbalanced Data
Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak Automatic Data Augmentation for Generalization in Reinforcement Learning
Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn Backward-Compatible Prediction Updates: A Probabilistic Approach
Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler Bandit Phase Retrieval
Tor Lattimore, Botao Hao Bandit Quickest Changepoint Detection
Aditya Gopalan, Braghadeesh Lakshminarayanan, Venkatesh Saligrama Bandits with Knapsacks Beyond the Worst Case
Karthik Abinav Sankararaman, Aleksandrs Slivkins Bandits with Many Optimal Arms
Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier Batch Active Learning at Scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar Batched Thompson Sampling
Cem Kalkanli, Ayfer Ozgur Bayesian Bellman Operators
Mattie Fellows, Kristian Hartikainen, Shimon Whiteson Bayesian Decision-Making Under Misspecified Priors with Applications to Meta-Learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire Bayesian Optimization with High-Dimensional Outputs
Wesley J Maddox, Maximilian Balandat, Andrew G Wilson, Eytan Bakshy BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-Francois Ton, Javier González, Yee W. Teh, Dino Sejdinovic Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao Bellman-Consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal Beltrami Flow and Neural Diffusion on Graphs
Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael Bronstein Best-Case Lower Bounds in Online Learning
Cristóbal Guzmán, Nishant Mehta, Ali Mortazavi Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal, Elias Benussi, Frederic Dreyer, Aleksandar Shtedritski, Yuki Asano Biological Learning in Key-Value Memory Networks
Danil Tyulmankov, Ching Fang, Annapurna Vadaparty, Guangyu Robert Yang Black Box Probabilistic Numerics
Onur Teymur, Christopher Foley, Philip Breen, Toni Karvonen, Chris J Oates BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng Blending Anti-Aliasing into Vision Transformer
Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia Boost Neural Networks by Checkpoints
Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, Xian Wei, Hairong Lv Boosted CVaR Classification
Runtian Zhai, Chen Dan, Arun Suggala, J. Zico Kolter, Pradeep K. Ravikumar Boosting with Multiple Sources
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh Bootstrap Your Object Detector via Mixed Training
Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai Bootstrapping the Error of Oja's Algorithm
Robert Lunde, Purnamrita Sarkar, Rachel Ward Breaking the Centralized Barrier for Cross-Device Federated Learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, Ananda Theertha Suresh Breaking the Dilemma of Medical Image-to-Image Translation
Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, Qichao Zhou Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho Bridging the Imitation Gap by Adaptive Insubordination
Luca Weihs, Unnat Jain, Iou-Jen Liu, Jordi Salvador, Svetlana Lazebnik, Aniruddha Kembhavi, Alex Schwing Bubblewrap: Online Tiling and Real-Time Flow Prediction on Neural Manifolds
Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John C. Pearson ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu Calibration and Consistency of Adversarial Surrogate Losses
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney, Vinay Kumar Verma, P. K. Srijith, Lawrence Carin, Piyush Rai Can Contrastive Learning Avoid Shortcut Solutions?
Joshua W. Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, Andrew McCallum CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve, Ronan Collobert, Alex Rogozhnikov Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Ide, Georgios Kollias, Dzung Phan, Naoki Abe CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard, Christopher Potts Causal Bandits with Unknown Graph Structure
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J Kusner, Ricardo Silva Causal Inference for Event Pairs in Multivariate Point Processes
Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Xiao Shou, Nicholas Mattei, Kristin P Bennett Causal Navigation by Continuous-Time Neural Networks
Charles Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus CCVS: Context-Aware Controllable Video Synthesis
Guillaume Le Moing, Jean Ponce, Cordelia Schmid Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang Challenges and Opportunities in High Dimensional Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R Andersen, Jonathan Huggins, Aki Vehtari Characterizing Generalization Under Out-of-Distribution Shifts in Deep Metric Learning
Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Bjorn Ommer Characterizing Possible Failure Modes in Physics-Informed Neural Networks
Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, Michael W. Mahoney Characterizing the Risk of Fairwashing
Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Yi-Shan Wu, Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg Class-Incremental Learning via Dual Augmentation
Fei Zhu, Zhen Cheng, Xu-yao Zhang, Cheng-lin Liu CLIP-It! Language-Guided Video Summarization
Medhini Narasimhan, Anna Rohrbach, Trevor Darrell Clockwork Variational Autoencoders
Vaibhav Saxena, Jimmy Ba, Danijar Hafner Clustering Effect of Adversarial Robust Models
Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang Co-Evolution Transformer for Protein Contact Prediction
He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul Bennett, Jiawei Han, Xia Song CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R Varshney CogView: Mastering Text-to-Image Generation via Transformers
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang Collaborating with Humans Without Human Data
Dj Strouse, Kevin McKee, Matt Botvinick, Edward Hughes, Richard Everett Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Siheng Chen, Ya Zhang Collapsed Variational Bounds for Bayesian Neural Networks
Marcin Tomczak, Siddharth Swaroop, Andrew Foong, Richard Turner Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder Compositional Reinforcement Learning from Logical Specifications
Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur Compressed Video Contrastive Learning
Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen, Ping Luo Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer Computer-Aided Design as Language
Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti Conditional Generation Using Polynomial Expansions
Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis Conflict-Averse Gradient Descent for Multi-Task Learning
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu Conformal Time-Series Forecasting
Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers
Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel Constrained Robust Submodular Partitioning
Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A Bilmes Container: Context Aggregation Networks
Peng Gao, Jiasen Lu, Hongsheng Li, Roozbeh Mottaghi, Aniruddha Kembhavi Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Leme, Jon Schneider Continual Auxiliary Task Learning
Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White Continual Learning via Local Module Composition
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, Laurent Charlin Continual World: A Robotic Benchmark for Continual Reinforcement Learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien Taylor Continuous Doubly Constrained Batch Reinforcement Learning
Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J Smola Continuous Latent Process Flows
Ruizhi Deng, Marcus A Brubaker, Greg Mori, Andreas Lehrmann Continuous Mean-Covariance Bandits
Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang Contrastive Active Inference
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt Contrastive Laplacian Eigenmaps
Hao Zhu, Ke Sun, Peter Koniusz Control Variates for Slate Off-Policy Evaluation
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat, Nathan Kallus Controlling Neural Networks with Rule Representations
Sungyong Seo, Sercan Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister Convergence Rates of Stochastic Gradient Descent Under Infinite Noise Variance
Hongjian Wang, Mert Gurbuzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A Erdogdu Convex Polytope Trees
Mohammadreza Armandpour, Ali Sadeghian, Mingyuan Zhou Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback
Lin Yang, Yu-Zhen Janice Chen, Stephen Pasteris, Mohammad Hajiesmaili, John C. S. Lui, Don Towsley Coordinated Proximal Policy Optimization
Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, Haiyin Piao, Hankz Hankui Zhuo Coresets for Clustering with Missing Values
Vladimir Braverman, Shaofeng Jiang, Robert Krauthgamer, Xuan Wu Coresets for Decision Trees of Signals
Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan I Newman, Dan Feldman Coresets for Time Series Clustering
Lingxiao Huang, K Sudhir, Nisheeth Vishnoi Corruption Robust Active Learning
Yifang Chen, Simon S Du, Kevin G. Jamieson CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado Cortico-Cerebellar Networks as Decoupling Neural Interfaces
Joseph Pemberton, Ellen Boven, Richard Apps, Rui Ponte Costa Counterfactual Explanations Can Be Manipulated
Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh Coupled Segmentation and Edge Learning via Dynamic Graph Propagation
Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas Breuel, Anima Anandkumar, Jan Kautz Credal Self-Supervised Learning
Julian Lienen, Eyke Hüllermeier Credit Assignment in Neural Networks Through Deep Feedback Control
Alexander Meulemans, Matilde Tristany Farinha, Javier Garcia Ordonez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe CrypTen: Secure Multi-Party Computation Meets Machine Learning
Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten Curriculum Disentangled Recommendation with Noisy Multi-Feedback
Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu Curriculum Offline Imitating Learning
Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu Dangers of Bayesian Model Averaging Under Covariate Shift
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew G Wilson Data Augmentation Can Improve Robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy A Mann Data Driven Semi-Supervised Learning
Maria-Florina F Balcan, Dravyansh Sharma Data Sharing and Compression for Cooperative Networked Control
Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang Decentralized Learning in Online Queuing Systems
Flore Sentenac, Etienne Boursier, Vianney Perchet Decentralized Q-Learning in Zero-Sum Markov Games
Muhammed Sayin, Kaiqing Zhang, David Leslie, Tamer Basar, Asuman Ozdaglar Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch Deconvolutional Networks on Graph Data
Jia Li, Jiajin Li, Yang Liu, Jianwei Yu, Yueting Li, Hong Cheng Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen Deep Conditional Gaussian Mixture Model for Constrained Clustering
Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia Vogt Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J Smola, Bernie Wang, Tim Januschowski Deep Inference of Latent Dynamics with Spatio-Temporal Super-Resolution Using Selective Backpropagation Through Time
Feng Zhu, Andrew Sedler, Harrison A Grier, Nauman Ahad, Mark Davenport, Matthew Kaufman, Andrea Giovannucci, Chethan Pandarinath Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang Deep Networks Provably Classify Data on Curves
Tingran Wang, Sam Buchanan, Dar Gilboa, John Wright Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc Bellemare Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian Deep Self-Dissimilarities as Powerful Visual Fingerprints
Idan Kligvasser, Tamar Shaham, Yuval Bahat, Tomer Michaeli DeepGEM: Generalized Expectation-Maximization for Blind Inversion
Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman DeepReduce: A Sparse-Tensor Communication Framework for Federated Deep Learning
Hang Xu, Kelly Kostopoulou, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia Denoising Normalizing Flow
Christian Horvat, Jean-Pascal Pfister Dense Keypoints via Multiview Supervision
Zhixuan Yu, Haozheng Yu, Long Sha, Sujoy Ganguly, Hyun Soo Park Dense Unsupervised Learning for Video Segmentation
Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth Densely Connected Normalizing Flows
Matej Grcić, Ivan Grubišić, Siniša Šegvić Designing Counterfactual Generators Using Deep Model Inversion
Jayaraman Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang, Sameh Khamis, Or Litany, Sanja Fidler DiBS: Differentiable Bayesian Structure Learning
Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause Differentiable Learning Under Triage
Nastaran Okati, Abir De, Manuel Rodriguez Differentiable Multiple Shooting Layers
Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama Differentiable Quality Diversity
Matthew Fontaine, Stefanos Nikolaidis Differentiable Rendering with Perturbed Optimizers
Quentin Le Lidec, Ivan Laptev, Cordelia Schmid, Justin Carpentier Differentiable Simulation of Soft Multi-Body Systems
Yiling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin Differentiable Spline Approximations
Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde Differentiable Unsupervised Feature Selection Based on a Gated Laplacian
Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger Differential Privacy over Riemannian Manifolds
Matthew Reimherr, Karthik Bharath, Carlos Soto Differentially Private Learning with Adaptive Clipping
Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy Differentially Private Model Personalization
Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta Differentially Private N-Gram Extraction
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin Differentially Private Sampling from Distributions
Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg Diffusion Normalizing Flow
Qinsheng Zhang, Yongxin Chen Dimension-Free Empirical Entropy Estimation
Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer Direct Multi-View Multi-Person 3D Pose Estimation
Tao Wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng Directed Graph Contrastive Learning
Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang Directed Probabilistic Watershed
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu Discovering and Achieving Goals via World Models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado P van Hasselt, David Silver, Satinder P. Singh Discrete-Valued Neural Communication
Dianbo Liu, Alex M Lamb, Kenji Kawaguchi, Anirudh Goyal ALIAS PARTH Goyal, Chen Sun, Michael Mozer, Yoshua Bengio Disentangled Contrastive Learning on Graphs
Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen Distilling Object Detectors with Feature Richness
Du Zhixing, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen Distributed Deep Learning in Open Collaborations
Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry V. Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, Gennady Pekhimenko Distributed Saddle-Point Problems Under Data Similarity
Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov Distributionally Robust Imitation Learning
Mohammad Ali Bashiri, Brian Ziebart, Xinhua Zhang Diverse Message Passing for Attribute with Heterophily
Liang Yang, Mengzhe Li, Liyang Liu, Bingxin Niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo Diversity Matters When Learning from Ensembles
Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip Torr, Luca Bertinetto Do Neural Optimal Transport Solvers Work? a Continuous Wasserstein-2 Benchmark
Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M Solomon, Alexander Filippov, Evgeny Burnaev Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu Do Vision Transformers See like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec, Guillaume Lample DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, Andrew G Wilson DominoSearch: Find Layer-Wise Fine-Grained N:M Sparse Schemes from Dense Neural Networks
Wei Sun, Aojun Zhou, Sander Stuijk, Rob Wijnhoven, Andrew Oakleigh Nelson, Hongsheng Li, Henk Corporaal DRIVE: One-Bit Distributed Mean Estimation
Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher DRONE: Data-Aware Low-Rank Compression for Large NLP Models
Patrick Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Mikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith Hengen, Michal Valko, Eva Dyer DSelect-K: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed Chi Dual-Stream Network for Visual Recognition
Mingyuan Mao, Peng Gao, Renrui Zhang, Honghui Zheng, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han Dueling Bandits with Team Comparisons
Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, Lei Li Dynaboard: An Evaluation-as-a-Service Platform for Holistic Next-Generation Benchmarking
Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush, Somya Jain, Ledell Wu, Robin Jia, Christopher Potts, Adina Williams, Douwe Kiela Dynamic Bottleneck for Robust Self-Supervised Exploration
Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang Dynamic Causal Bayesian Optimization
Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard J. Radke Dynamic Grained Encoder for Vision Transformers
Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng Dynamic Inference with Neural Interpreters
Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf Dynamic Resolution Network
Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang Dynamic Trace Estimation
Prathamesh Dharangutte, Christopher Musco Dynamical Wasserstein Barycenters for Time-Series Modeling
Kevin Cheng, Shuchin Aeron, Michael C Hughes, Eric L Miller DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling Early Convolutions Help Transformers See Better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollar, Ross B. Girshick Edge Representation Learning with Hypergraphs
Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, Sung Ju Hwang EditGAN: High-Precision Semantic Image Editing
Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler Editing a Classifier by Rewriting Its Prediction Rules
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry Efficient and Accurate Gradients for Neural SDEs
Patrick Kidger, James Foster, Xuechen Li, Terry Lyons Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos Efficient Equivariant Network
Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin Efficient Hierarchical Bayesian Inference for Spatio-Temporal Regression Models in Neuroimaging
Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe Efficient Training of Visual Transformers with Small Datasets
Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco Nadai Efficient Truncated Linear Regression with Unknown Noise Variance
Constantinos Daskalakis, Patroklos Stefanou, Rui Yao, Emmanouil Zampetakis Efficiently Identifying Task Groupings for Multi-Task Learning
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao ELLA: Exploration Through Learned Language Abstraction
Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh Emergent Discrete Communication in Semantic Spaces
Mycal Tucker, Huao Li, Siddharth Agrawal, Dana Hughes, Katia Sycara, Michael Lewis, Julie A Shah End-to-End Weak Supervision
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski Ensembling Graph Predictions for AMR Parsing
Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam Nguyen, Dzung Phan, Vanessa Lopez, Ramon Fernandez Astudillo Entropic Desired Dynamics for Intrinsic Control
Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih Entropy-Based Adaptive Hamiltonian Monte Carlo
Marcel Hirt, Michalis K. Titsias, Petros Dellaportas Environment Generation for Zero-Shot Compositional Reinforcement Learning
Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust Episodic Multi-Agent Reinforcement Learning with Curiosity-Driven Exploration
Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang Equivariant Manifold Flows
Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser Nam Lim, Christopher M De Sa Estimating the Long-Term Effects of Novel Treatments
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis Estimating the Unique Information of Continuous Variables
Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman Evaluating Efficient Performance Estimators of Neural Architectures
Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, Yu Wang Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
Ho Chit Siu, Jaime Peña, Edenna Chen, Yutai Zhou, Victor Lopez, Kyle Palko, Kimberlee Chang, Ross Allen Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Jagdeep Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik Explaining Heterogeneity in Medial Entorhinal Cortex with Task-Driven Neural Networks
Aran Nayebi, Alexander Attinger, Malcolm Campbell, Kiah Hardcastle, Isabel Low, Caitlin S Mallory, Gabriel Mel, Ben Sorscher, Alex H Williams, Surya Ganguli, Lisa Giocomo, Dan Yamins Explaining Hyperparameter Optimization via Partial Dependence Plots
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar Explicable Reward Design for Reinforcement Learning Agents
Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla Exploiting Opponents Under Utility Constraints in Sequential Games
Martino Bernasconi-de-Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi, Francesco Trovò Exploring Forensic Dental Identification with Deep Learning
Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting Shao, Kun Wang, Lei He Extracting Deformation-Aware Local Features by Learning to Deform
Guilherme Potje, Renato Martins, Felipe Chamone, Erickson Nascimento FACMAC: Factored Multi-Agent Centralised Policy Gradients
Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Boehmer, Shimon Whiteson Fair Clustering Under a Bounded Cost
Seyed Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson Fair Sortition Made Transparent
Bailey Flanigan, Gregory Kehne, Ariel D Procaccia Fairness in Ranking Under Uncertainty
Ashudeep Singh, David Kempe, Thorsten Joachims Fairness via Representation Neutralization
Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Awadallah, Xia Hu Fast Abductive Learning by Similarity-Based Consistency Optimization
Yu-Xuan Huang, Wang-Zhou Dai, Le-Wen Cai, Stephen H Muggleton, Yuan Jiang Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen, Uthaipon Tantipongpipat Fast Axiomatic Attribution for Neural Networks
Robin Hesse, Simone Schaub-Meyer, Stefan Roth Fast Certified Robust Training with Short Warmup
Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh Fast Pure Exploration via Frank-Wolfe
Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F Balcan, Virginia Smith, Ameet Talwalkar Federated Linear Contextual Bandits
Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen Federated Multi-Task Learning Under a Mixture of Distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal Federated Reconstruction: Partially Local Federated Learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, Sushant Prakash Federated-EM with Heterogeneity Mitigation and Variance Reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin Few-Round Learning for Federated Learning
Younghyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon Few-Shot Object Detection via Association and DIscrimination
Yuhang Cao, Jiaqi Wang, Ying Jin, Tong Wu, Kai Chen, Ziwei Liu, Dahua Lin FINE Samples for Learning with Noisy Labels
Taehyeon Kim, Jongwoo Ko, Sangwook Cho, JinHwan Choi, Se-Young Yun Fine-Grained Zero-Shot Learning with DNA as Side Information
Sarkhan Badirli, Zeynep Akata, George Mohler, Christine Picard, Mehmet M Dundar FjORD: Fair and Accurate Federated Learning Under Heterogeneous Targets with Ordered Dropout
Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, Nicholas Lane FLEX: Unifying Evaluation for Few-Shot NLP
Jonathan Bragg, Arman Cohan, Kyle Lo, Iz Beltagy Flexible Option Learning
Martin Klissarov, Doina Precup FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki Focal Attention for Long-Range Interactions in Vision Transformers
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao Foundations of Symbolic Languages for Model Interpretability
Marcelo Arenas, Daniel Báez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Alexander Camuto, George Deligiannidis, Murat A Erdogdu, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu Framing RNN as a Kernel Method: A Neural ODE Approach
Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau Fuzzy Clustering with Similarity Queries
Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal Gauge Equivariant Transformer
Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza Generalized DataWeighting via Class-Level Gradient Manipulation
Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue Liu, Hao Liu, Dejing Dou Generalized Shape Metrics on Neural Representations
Alex H Williams, Erin Kunz, Simon Kornblith, Scott Linderman Generative vs. Discriminative: Rethinking the Meta-Continual Learning
Mohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, Mahdieh Soleymani Generic Neural Architecture Search via Regression
Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen Geometry Processing with Neural Fields
Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, Tommi Jaakkola Glance-and-Gaze Vision Transformer
Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan L. Yuille, Wei Shen Global Filter Networks for Image Classification
Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
Kibeom Kim, Min Whoo Lee, Yoonsung Kim, JeHwan Ryu, Minsu Lee, Byoung-Tak Zhang Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham Kakade, Jason Lee, Qi Lei, Runzhe Wang, Jiaqi Yang Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade Good Classification Measures and How to Find Them
Martijn Gösgens, Anton Zhiyanov, Aleksey Tikhonov, Liudmila Prokhorenkova Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie Grammar-Based Grounded Lexicon Learning
Jiayuan Mao, Freda Shi, Jiajun Wu, Roger P. Levy, Josh Tenenbaum Graph Adversarial Self-Supervised Learning
Longqi Yang, Liangliang Zhang, Wenjing Yang Graph Neural Networks with Adaptive Residual
Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang Graph Neural Networks with Local Graph Parameters
Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann GraphFormers: GNN-Nested Transformers for Representation Learning on Textual Graph
Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie Graphical Models in Heavy-Tailed Markets
Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar GRIN: Generative Relation and Intention Network for Multi-Agent Trajectory Prediction
Longyuan Li, Jian Yao, Li Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David P. Wipf, Zheng Zhang Grounding Spatio-Temporal Language with Transformers
Tristan Karch, Laetitia Teodorescu, Katja Hofmann, Clément Moulin-Frier, Pierre-Yves Oudeyer Group Equivariant Subsampling
Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee W. Teh Habitat 2.0: Training Home Assistants to Rearrange Their Habitat
Andrew Szot, Alexander Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Singh Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimír Vondruš, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, Dhruv Batra Hard-Attention for Scalable Image Classification
Athanasios Papadopoulos, Pawel Korus, Nasir Memon Hash Layers for Large Sparse Models
Stephen Roller, Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston Heavy Ball Momentum for Conditional Gradient
Bingcong Li, Alireza Sadeghi, Georgios Giannakis Heavy Ball Neural Ordinary Differential Equations
Hedi Xia, Vai Suliafu, Hangjie Ji, Tan Nguyen, Andrea Bertozzi, Stanley Osher, Bao Wang Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
Melih Barsbey, Milad Sefidgaran, Murat A Erdogdu, Gaël Richard, Umut Simsekli Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan Hierarchical Skills for Efficient Exploration
Jonas Gehring, Gabriel Synnaeve, Andreas Krause, Nicolas Usunier Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Horvath, Theodoros Damoulas, Terry Lyons HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort How Can Classical Multidimensional Scaling Go Wrong?
Rishi Sonthalia, Greg Van Buskirk, Benjamin Raichel, Anna Gilbert How Does It Sound?
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