ICLR 2021

860 papers

Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning Ruozi Huang, Huang Hu, Wei Wu, Kei Sawada, Mi Zhang, Daxin Jiang

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$i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

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A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning Samuel Horváth, Peter Richtarik

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A Block Minifloat Representation for Training Deep Neural Networks Sean Fox, Seyedramin Rasoulinezhad, Julian Faraone, David Boland, Philip Leong

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A Critique of Self-Expressive Deep Subspace Clustering Benjamin David Haeffele, Chong You, Rene Vidal

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A Design Space Study for LISTA and Beyond Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

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A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima Zeke Xie, Issei Sato, Masashi Sugiyama

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A Discriminative Gaussian Mixture Model with Sparsity Hideaki Hayashi, Seiichi Uchida

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A Distributional Approach to Controlled Text Generation Muhammad Khalifa, Hady Elsahar, Marc Dymetman

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A Geometric Analysis of Deep Generative Image Models and Its Applications Binxu Wang, Carlos R Ponce

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A Good Image Generator Is What You Need for High-Resolution Video Synthesis Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, Sergey Tulyakov

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A Gradient Flow Framework for Analyzing Network Pruning Ekdeep Singh Lubana, Robert P. Dick

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A Hypergradient Approach to Robust Regression Without Correspondence Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha

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A Learning Theoretic Perspective on Local Explainability Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar

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A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora

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A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Renjie Liao, Raquel Urtasun, Richard Zemel

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A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference Sanghyun Hong, Yigitcan Kaya, Ionuț-Vlad Modoranu, Tudor Dumitras

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A Statistical Theory of Cold Posteriors in Deep Neural Networks Laurence Aitchison

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A Teacher-Student Framework to Distill Future Trajectories Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf

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A Temporal Kernel Approach for Deep Learning with Continuous-Time Information Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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A Trainable Optimal Transport Embedding for Feature Aggregation and Its Relationship to Attention Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal

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A Unified Approach to Interpreting and Boosting Adversarial Transferability Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang

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A Unifying View on Implicit Bias in Training Linear Neural Networks Chulhee Yun, Shankar Krishnan, Hossein Mobahi

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A Universal Representation Transformer Layer for Few-Shot Image Classification Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle

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A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels Leon Lang, Maurice Weiler

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Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng, Qi Feng, Georgios P. Karagiannis, Guang Lin, Faming Liang

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Accurate Learning of Graph Representations with Graph Multiset Pooling Jinheon Baek, Minki Kang, Sung Ju Hwang

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Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning Haibo Yang, Minghong Fang, Jia Liu

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Acting in Delayed Environments with Non-Stationary Markov Policies Esther Derman, Gal Dalal, Shie Mannor

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Activation-Level Uncertainty in Deep Neural Networks Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato

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Active Contrastive Learning of Audio-Visual Video Representations Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song

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AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogerio Feris

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AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Ke Sun, Zhanxing Zhu, Zhouchen Lin

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AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-Invariant Weights Byeongho Heo, Sanghyuk Chun, Seong Joon Oh, Dongyoon Han, Sangdoo Yun, Gyuwan Kim, Youngjung Uh, Jung-Woo Ha

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Adapting to Reward Progressivity via Spectral Reinforcement Learning Michael Dann, John Thangarajah

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Adaptive and Generative Zero-Shot Learning Yu-Ying Chou, Hsuan-Tien Lin, Tyng-Luh Liu

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Adaptive Extra-Gradient Methods for Min-Max Optimization and Games Kimon Antonakopoulos, Veronica Belmega, Panayotis Mertikopoulos

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Adaptive Federated Optimization Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, Hugh Brendan McMahan

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Adaptive Procedural Task Generation for Hard-Exploration Problems Kuan Fang, Yuke Zhu, Silvio Savarese, L. Fei-Fei

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Adaptive Universal Generalized PageRank Graph Neural Network Eli Chien, Jianhao Peng, Pan Li, Olgica Milenkovic

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AdaSpeech: Adaptive Text to Speech for Custom Voice Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng Zhao, Tie-Yan Liu

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Adversarial Score Matching and Improved Sampling for Image Generation Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes

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Adversarially Guided Actor-Critic Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

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Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney

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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Cote, Yonatan Bisk, Adam Trischler, Matthew Hausknecht

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Aligning AI with Shared Human Values Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt

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An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

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An Unsupervised Deep Learning Approach for Real-World Image Denoising Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao

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Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, Paul Honeine

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Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics Vinay Venkatesh Ramasesh, Ethan Dyer, Maithra Raghu

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Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies Paul Pu Liang, Manzil Zaheer, Yuan Wang, Amr Ahmed

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ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning Hengrui Cai, Rui Song, Wenbin Lu

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Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval Wenhan Xiong, Xiang Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Scott Yih, Sebastian Riedel, Douwe Kiela, Barlas Oguz

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Anytime Sampling for Autoregressive Models via Ordered Autoencoding Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

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Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, Arnold Overwijk

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Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber

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Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees? Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

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Are Wider Nets Better Given the Same Number of Parameters? Anna Golubeva, Guy Gur-Ari, Behnam Neyshabur

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ARMOURED: Adversarially Robust MOdels Using Unlabeled Data by REgularizing Diversity Kangkang Lu, Cuong Manh Nguyen, Xun Xu, Kiran Krishnamachari, Yu Jing Goh, Chuan-Sheng Foo

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Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning Valerie Chen, Abhinav Gupta, Kenneth Marino

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Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method Using Deep Denoising Priors Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek Kamilov

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Attentional Constellation Nets for Few-Shot Learning Weijian Xu, Yifan Xu, Huaijin Wang, Zhuowen Tu

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Auction Learning as a Two-Player Game Jad Rahme, Samy Jelassi, S. Matthew Weinberg

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Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bezenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

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Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai

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AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini, Arvind Krishnamurthy

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Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization Michael R Zhang, Thomas Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

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Autoregressive Entity Retrieval Nicola De Cao, Gautier Izacard, Sebastian Riedel, Fabio Petroni

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Auxiliary Learning by Implicit Differentiation Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

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Auxiliary Task Update Decomposition: The Good, the Bad and the Neutral Lucio M. Dery, Yann Dauphin, David Grangier

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Average-Case Acceleration for Bilinear Games and Normal Matrices Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

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Bag of Tricks for Adversarial Training Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu

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Balancing Constraints and Rewards with Meta-Gradient D4PG Dan A. Calian, Daniel J Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy Mann

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Batch Reinforcement Learning Through Continuation Method Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen

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Bayesian Context Aggregation for Neural Processes Michael Volpp, Fabian Flürenbrock, Lukas Grossberger, Christian Daniel, Gerhard Neumann

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Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake Snell, Richard Zemel

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Behavioral Cloning from Noisy Demonstrations Fumihiro Sasaki, Ryota Yamashina

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Benchmarks for Deep Off-Policy Evaluation Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Thomas Paine

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Benefit of Deep Learning with Non-Convex Noisy Gradient Descent: Provable Excess Risk Bound and Superiority to Kernel Methods Taiji Suzuki, Shunta Akiyama

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BERTology Meets Biology: Interpreting Attention in Protein Language Models Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong, Richard Socher, Nazneen Rajani

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Better Fine-Tuning by Reducing Representational Collapse Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, Sonal Gupta

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Beyond Categorical Label Representations for Image Classification Boyuan Chen, Yu Li, Sunand Raghupathi, Hod Lipson

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Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters Aston Zhang, Yi Tay, Shuai Zhang, Alvin Chan, Anh Tuan Luu, Siu Hui, Jie Fu

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Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech Yoonhyung Lee, Joongbo Shin, Kyomin Jung

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BiPointNet: Binary Neural Network for Point Clouds Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Liu, Hao Su

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Blending MPC & Value Function Approximation for Efficient Reinforcement Learning Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots

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BOIL: Towards Representation Change for Few-Shot Learning Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, Se-Young Yun

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Boost Then Convolve: Gradient Boosting Meets Graph Neural Networks Sergei Ivanov, Liudmila Prokhorenkova

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Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis Zhipeng Bao, Yu-Xiong Wang, Martial Hebert

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BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, Shi Gu

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BREEDS: Benchmarks for Subpopulation Shift Shibani Santurkar, Dimitris Tsipras, Aleksander Madry

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BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization Huanrui Yang, Lin Duan, Yiran Chen, Hai Li

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BUSTLE: Bottom-up Program Synthesis Through Learning-Guided Exploration Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

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Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou, Steven Wu, Arindam Banerjee

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Byzantine-Resilient Non-Convex Stochastic Gradient Descent Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh

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C-Learning: Horizon-Aware Cumulative Accessibility Estimation Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg

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C-Learning: Learning to Achieve Goals via Recursive Classification Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

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Calibration of Neural Networks Using Splines Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley

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Calibration Tests Beyond Classification David Widmann, Fredrik Lindsten, Dave Zachariah

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Can a Fruit Fly Learn Word Embeddings? Yuchen Liang, Chaitanya Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J Zaki, Dmitry Krotov

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CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang

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Capturing Label Characteristics in VAEs Tom Joy, Sebastian Schmon, Philip Torr, Siddharth N, Tom Rainforth

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Categorical Normalizing Flows via Continuous Transformations Phillip Lippe, Efstratios Gavves

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CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer

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CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, Z. Jane Wang

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Certify or Predict: Boosting Certified Robustness with Compositional Architectures Mark Niklas Mueller, Mislav Balunovic, Martin Vechev

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Chaos of Learning Beyond Zero-Sum and Coordination via Game Decompositions Yun Kuen Cheung, Yixin Tao

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Characterizing Signal Propagation to Close the Performance Gap in Unnormalized ResNets Andrew Brock, Soham De, Samuel L Smith

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ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, Deepak Gupta

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Clairvoyance: A Pipeline Toolkit for Medical Time Series Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar

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Class Normalization for (Continual)? Generalized Zero-Shot Learning Ivan Skorokhodov, Mohamed Elhoseiny

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Clustering-Friendly Representation Learning via Instance Discrimination and Feature Decorrelation Yaling Tao, Kentaro Takagi, Kouta Nakata

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Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity JangHyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song

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CO2: Consistent Contrast for Unsupervised Visual Representation Learning Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille

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COCO: Controllable Counterfactuals for Evaluating Dialogue State Trackers Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Rajani, Xifeng Yan, Yingbo Zhou, Caiming Xiong

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CoCon: A Self-Supervised Approach for Controlled Text Generation Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

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CoDA: Contrast-Enhanced and Diversity-Promoting Data Augmentation for Natural Language Understanding Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Weizhu Chen, Jiawei Han

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Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

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Colorization Transformer Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner

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Combining Ensembles and Data Augmentation Can Harm Your Calibration Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

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Combining Label Propagation and Simple Models Out-Performs Graph Neural Networks Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin Benson

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Combining Physics and Machine Learning for Network Flow Estimation Arlei Lopes da Silva, Furkan Kocayusufoglu, Saber Jafarpour, Francesco Bullo, Ananthram Swami, Ambuj Singh

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Communication in Multi-Agent Reinforcement Learning: Intention Sharing Woojun Kim, Jongeui Park, Youngchul Sung

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Complex Query Answering with Neural Link Predictors Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez

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CompOFA – Compound Once-for-All Networks for Faster Multi-Platform Deployment Manas Sahni, Shreya Varshini, Alind Khare, Alexey Tumanov

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Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach, Shai Shalev-Shwartz

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Concept Learners for Few-Shot Learning Kaidi Cao, Maria Brbic, Jure Leskovec

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Conditional Generative Modeling via Learning the Latent Space Sameera Ramasinghe, Kanchana Nisal Ranasinghe, Salman Khan, Nick Barnes, Stephen Gould

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Conditional Negative Sampling for Contrastive Learning of Visual Representations Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman

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Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data Jonathan Pilault, Amine El hattami, Christopher Pal

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Conformation-Guided Molecular Representation with Hamiltonian Neural Networks Ziyao Li, Shuwen Yang, Guojie Song, Lingsheng Cai

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Conservative Safety Critics for Exploration Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg

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Contemplating Real-World Object Classification Ali Borji

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Contextual Dropout: An Efficient Sample-Dependent Dropout Module Xinjie Fan, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, Mingyuan Zhou

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Contextual Transformation Networks for Online Continual Learning Quang Pham, Chenghao Liu, Doyen Sahoo, Steven Hoi

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Continual Learning in Recurrent Neural Networks Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F Grewe

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Continuous Wasserstein-2 Barycenter Estimation Without Minimax Optimization Alexander Korotin, Lingxiao Li, Justin Solomon, Evgeny Burnaev

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Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

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Contrastive Divergence Learning Is a Time Reversal Adversarial Game Omer Yair, Tomer Michaeli

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Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions Zhengxian Lin, Kin-Ho Lam, Alan Fern

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Contrastive Learning with Adversarial Perturbations for Conditional Text Generation Seanie Lee, Dong Bok Lee, Sung Ju Hwang

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Contrastive Learning with Hard Negative Samples Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka

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Contrastive Syn-to-Real Generalization Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, Jose M. Alvarez, Zhangyang Wang, Anima Anandkumar

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Control-Aware Representations for Model-Based Reinforcement Learning Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh

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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville

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Convex Regularization Behind Neural Reconstruction Arda Sahiner, Morteza Mardani, Batu Ozturkler, Mert Pilanci, John M. Pauly

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Coping with Label Shift via Distributionally Robust Optimisation Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

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CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei

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Correcting Experience Replay for Multi-Agent Communication Sanjeevan Ahilan, Peter Dayan

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Counterfactual Generative Networks Axel Sauer, Andreas Geiger

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Coupled Oscillatory Recurrent Neural Network (coRNN): An Accurate and (gradient) Stable Architecture for Learning Long Time Dependencies T. Konstantin Rusch, Siddhartha Mishra

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CPR: Classifier-Projection Regularization for Continual Learning Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio Calmon, Taesup Moon

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CPT: Efficient Deep Neural Network Training via Cyclic Precision Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

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Creative Sketch Generation Songwei Ge, Vedanuj Goswami, Larry Zitnick, Devi Parikh

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Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization Jun-Tae Lee, Mihir Jain, Hyoungwoo Park, Sungrack Yun

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CT-Net: Channel Tensorization Network for Video Classification Kunchang Li, Xianhang Li, Yali Wang, Jun Wang, Yu Qiao

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Cut Out the Annotator, Keep the Cutout: Better Segmentation with Weak Supervision Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re

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DARTS-: Robustly Stepping Out of Performance Collapse Without Indicators Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, Junchi Yan

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Data-Efficient Reinforcement Learning with Self-Predictive Representations Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, Philip Bachman

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Dataset Condensation with Gradient Matching Bo Zhao, Konda Reddy Mopuri, Hakan Bilen

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Dataset Inference: Ownership Resolution in Machine Learning Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

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Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen, Zhourong Chen, Jaehoon Lee

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DC3: A Learning Method for Optimization with Hard Constraints Priya L. Donti, David Rolnick, J Zico Kolter

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DDPNOpt: Differential Dynamic Programming Neural Optimizer Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou

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DeBERTa: Decoding-Enhanced BERT with Disentangled Attention Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen

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Debiasing Concept-Based Explanations with Causal Analysis Mohammad Taha Bahadori, David Heckerman

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Decentralized Attribution of Generative Models Changhoon Kim, Yi Ren, Yezhou Yang

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Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet

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Deconstructing the Regularization of BatchNorm Yann Dauphin, Ekin Dogus Cubuk

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Decoupling Global and Local Representations via Invertible Generative Flows Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard H Hovy

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Deep Encoder, Shallow Decoder: Reevaluating Non-Autoregressive Machine Translation Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah Smith

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Deep Equals Shallow for ReLU Networks in Kernel Regimes Alberto Bietti, Francis Bach

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Deep Learning Meets Projective Clustering Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman

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Deep Networks and the Multiple Manifold Problem Sam Buchanan, Dar Gilboa, John Wright

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Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas, Yuxuan Zhang, Florian Kerschbaum

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Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS Lin Chen, Sheng Xu

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Deep Partition Aggregation: Provable Defenses Against General Poisoning Attacks Alexander Levine, Soheil Feizi

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Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition Seon-Ho Lee, Chang-Su Kim

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Deep Symbolic Regression: Recovering Mathematical Expressions from Data via Risk-Seeking Policy Gradients Brenden K Petersen, Mikel Landajuela Larma, Terrell N. Mundhenk, Claudio Prata Santiago, Soo Kyung Kim, Joanne Taery Kim

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DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs Aayam Kumar Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern

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Deformable DETR: Deformable Transformers for End-to-End Object Detection Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai

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Degree-Quant: Quantization-Aware Training for Graph Neural Networks Shyam Anil Tailor, Javier Fernandez-Marques, Nicholas Donald Lane

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DeLighT: Deep and Light-Weight Transformer Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi

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Denoising Diffusion Implicit Models Jiaming Song, Chenlin Meng, Stefano Ermon

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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

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DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth, Alan Black, Yulia Tsvetkov

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DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation Alexandre Rame, Matthieu Cord

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Differentiable Segmentation of Sequences Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller

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Differentiable Trust Region Layers for Deep Reinforcement Learning Fabian Otto, Philipp Becker, Vien Anh Ngo, Hanna Carolin Maria Ziesche, Gerhard Neumann

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Differentially Private Learning Needs Better Features (or Much More Data) Florian Tramer, Dan Boneh

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DiffWave: A Versatile Diffusion Model for Audio Synthesis Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro

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DINO: A Conditional Energy-Based GAN for Domain Translation Konstantinos Vougioukas, Stavros Petridis, Maja Pantic

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Directed Acyclic Graph Neural Networks Veronika Thost, Jie Chen

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Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

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Disambiguating Symbolic Expressions in Informal Documents Dennis Müller, Cezary Kaliszyk

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Discovering a Set of Policies for the Worst Case Reward Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh

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Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Shaolei Du, Yu Wang, Yi Wu

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Discovering Non-Monotonic Autoregressive Orderings with Variational Inference Xuanlin Li, Brandon Trabucco, Dong Huk Park, Michael Luo, Sheng Shen, Trevor Darrell, Yang Gao

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Discrete Graph Structure Learning for Forecasting Multiple Time Series Chao Shang, Jie Chen, Jinbo Bi

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Disentangled Recurrent Wasserstein Autoencoder Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang

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Disentangling 3D Prototypical Networks for Few-Shot Concept Learning Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley, Katerina Fragkiadaki

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Distance-Based Regularisation of Deep Networks for Fine-Tuning Henry Gouk, Timothy Hospedales, Massimiliano Pontil

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Distilling Knowledge from Reader to Retriever for Question Answering Gautier Izacard, Edouard Grave

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Distributed Momentum for Byzantine-Resilient Stochastic Gradient Descent El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault

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Distributional Sliced-Wasserstein and Applications to Generative Modeling Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui

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Diverse Video Generation Using a Gaussian Process Trigger Gaurav Shrivastava, Abhinav Shrivastava

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Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo

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Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu

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Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth Thao Nguyen, Maithra Raghu, Simon Kornblith

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Does Enhanced Shape Bias Improve Neural Network Robustness to Common Corruptions? Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer, Jan Hendrik Metzen

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Domain Generalization with MixStyle Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

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Domain-Robust Visual Imitation Learning with Mutual Information Constraints Edoardo Cetin, Oya Celiktutan

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DOP: Off-Policy Multi-Agent Decomposed Policy Gradients Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang

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DrNAS: Dirichlet Neural Architecture Search Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh

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Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim

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Dual-Mode ASR: Unify and Improve Streaming ASR with Full-Context Modeling Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N Sainath, Yonghui Wu, Ruoming Pang

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Dynamic Tensor Rematerialization Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock

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DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation Minjia Zhang, Menghao Li, Chi Wang, Mingqin Li

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Early Stopping in Deep Networks: Double Descent and How to Eliminate It Reinhard Heckel, Fatih Furkan Yilmaz

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Economic Hyperparameter Optimization with Blended Search Strategy Chi Wang, Qingyun Wu, Silu Huang, Amin Saied

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EEC: Learning to Encode and Regenerate Images for Continual Learning Ali Ayub, Alan Wagner

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Effective Abstract Reasoning with Dual-Contrast Network Tao Zhuo, Mohan Kankanhalli

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Effective and Efficient Vote Attack on Capsule Networks Jindong Gu, Baoyuan Wu, Volker Tresp

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Effective Distributed Learning with Random Features: Improved Bounds and Algorithms Yong Liu, Jiankun Liu, Shuqiang Wang

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Efficient Certified Defenses Against Patch Attacks on Image Classifiers Jan Hendrik Metzen, Maksym Yatsura

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Efficient Conformal Prediction via Cascaded Inference with Expanded Admission Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay

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Efficient Continual Learning with Modular Networks and Task-Driven Priors Tom Veniat, Ludovic Denoyer, MarcAurelio Ranzato

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Efficient Empowerment Estimation for Unsupervised Stabilization Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin

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Efficient Generalized Spherical CNNs Oliver Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d'Avezac, Jason McEwen

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Efficient Inference of Flexible Interaction in Spiking-Neuron Networks Feng Zhou, Yixuan Zhang, Jun Zhu

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Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang

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Efficient Transformers in Reinforcement Learning Using Actor-Learner Distillation Emilio Parisotto, Russ Salakhutdinov

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Efficient Wasserstein Natural Gradients for Reinforcement Learning Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

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EigenGame: PCA as a Nash Equilibrium Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

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Emergent Road Rules in Multi-Agent Driving Environments Avik Pal, Jonah Philion, Yuan-Hong Liao, Sanja Fidler

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Emergent Symbols Through Binding in External Memory Taylor Whittington Webb, Ishan Sinha, Jonathan Cohen

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Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition Yangming Li, Lemao Liu, Shuming Shi

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Empirical or Invariant Risk Minimization? a Sample Complexity Perspective Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

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End-to-End Adversarial Text-to-Speech Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan

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End-to-End Egospheric Spatial Memory Daniel James Lenton, Stephen James, Ronald Clark, Andrew Davison

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Enforcing Robust Control Guarantees Within Neural Network Policies Priya L. Donti, Melrose Roderick, Mahyar Fazlyab, J Zico Kolter

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Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation Peiye Zhuang, Oluwasanmi O Koyejo, Alex Schwing

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Entropic Gradient Descent Algorithms and Wide Flat Minima Fabrizio Pittorino, Carlo Lucibello, Christoph Feinauer, Gabriele Perugini, Carlo Baldassi, Elizaveta Demyanenko, Riccardo Zecchina

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Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors Ali Harakeh, Steven L. Waslander

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Estimating Informativeness of Samples with Smooth Unique Information Hrayr Harutyunyan, Alessandro Achille, Giovanni Paolini, Orchid Majumder, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

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Estimating Lipschitz Constants of Monotone Deep Equilibrium Models Chirag Pabbaraju, Ezra Winston, J Zico Kolter

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Evaluating the Disentanglement of Deep Generative Models Through Manifold Topology Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon

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Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks Like Hui, Mikhail Belkin

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Evaluation of Similarity-Based Explanations Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui

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Evaluations and Methods for Explanation Through Robustness Analysis Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

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Evolving Reinforcement Learning Algorithms John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

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Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel

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Explainable Deep One-Class Classification Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus Robert Muller

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Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp

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Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning Alihan Hüyük, Daniel Jarrett, Cem Tekin, Mihaela van der Schaar

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Explaining the Efficacy of Counterfactually Augmented Data Divyansh Kaushik, Amrith Setlur, Eduard H Hovy, Zachary Chase Lipton

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Exploring Balanced Feature Spaces for Representation Learning Bingyi Kang, Yu Li, Sa Xie, Zehuan Yuan, Jiashi Feng

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Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

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Expressive Power of Invariant and Equivariant Graph Neural Networks Waiss Azizian, Marc Lelarge

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Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Georg Martius

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Extreme Memorization via Scale of Initialization Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur

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Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

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Fair Mixup: Fairness via Interpolation Ching-Yao Chuang, Youssef Mroueh

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FairBatch: Batch Selection for Model Fairness Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

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FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, Lawrence Carin

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Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers Sahil Singla, Soheil Feizi

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Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh

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Fast and Slow Learning of Recurrent Independent Mechanisms Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

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Fast Convergence of Stochastic Subgradient Method Under Interpolation Huang Fang, Zhenan Fan, Michael Friedlander

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Fast Geometric Projections for Local Robustness Certification Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, Corina Pasareanu

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Faster Binary Embeddings for Preserving Euclidean Distances Jinjie Zhang, Rayan Saab

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FastSpeech 2: Fast and High-Quality End-to-End Text to Speech Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu

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FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning Hong-You Chen, Wei-Lun Chao

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FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou

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Federated Learning Based on Dynamic Regularization Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, Venkatesh Saligrama

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Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

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Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang

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FedMix: Approximation of Mixup Under Mean Augmented Federated Learning Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang

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Few-Shot Bayesian Optimization with Deep Kernel Surrogates Martin Wistuba, Josif Grabocka

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Few-Shot Learning via Learning the Representation, Provably Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei

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Fidelity-Based Deep Adiabatic Scheduling Eli Ovits, Lior Wolf

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Filtered Inner Product Projection for Crosslingual Embedding Alignment Vin Sachidananda, Ziyi Yang, Chenguang Zhu

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Flowtron: An Autoregressive Flow-Based Generative Network for Text-to-Speech Synthesis Rafael Valle, Kevin J. Shih, Ryan Prenger, Bryan Catanzaro

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FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization Lanqing Li, Rui Yang, Dijun Luo

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Fooling a Complete Neural Network Verifier Dániel Zombori, Balázs Bánhelyi, Tibor Csendes, István Megyeri, Márk Jelasity

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For Self-Supervised Learning, Rationality Implies Generalization, Provably Yamini Bansal, Gal Kaplun, Boaz Barak

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Fourier Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

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Free Lunch for Few-Shot Learning: Distribution Calibration Shuo Yang, Lu Liu, Min Xu

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Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders Mangal Prakash, Alexander Krull, Florian Jug

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Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online Yangchen Pan, Kirby Banman, Martha White

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GAN "Steerability" Without Optimization Nurit Spingarn, Ron Banner, Tomer Michaeli

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GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon

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GANs Can Play Lottery Tickets Too Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen

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Gauge Equivariant Mesh CNNs: Anisotropic Convolutions on Geometric Graphs Pim De Haan, Maurice Weiler, Taco Cohen, Max Welling

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Generalization Bounds via Distillation Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang

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Generalization in Data-Driven Models of Primary Visual Cortex Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay Jagadish, Eric Wang, Edgar Y. Walker, Santiago A Cadena, Taliah Muhammad, Erick Cobos, Andreas S. Tolias, Alexander S Ecker, Fabian H. Sinz

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Generalized Energy Based Models Michael Arbel, Liang Zhou, Arthur Gretton

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Generalized Multimodal ELBO Thomas M. Sutter, Imant Daunhawer, Julia E Vogt

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Generalized Variational Continual Learning Noel Loo, Siddharth Swaroop, Richard E Turner

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Generating Adversarial Computer Programs Using Optimized Obfuscations Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, Una-May O'Reilly

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Generating Furry Cars: Disentangling Object Shape and Appearance Across Multiple Domains Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee

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Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule Shuhei Kurita, Kyunghyun Cho

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Generative Scene Graph Networks Fei Deng, Zhuo Zhi, Donghun Lee, Sungjin Ahn

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Generative Time-Series Modeling with Fourier Flows Ahmed Alaa, Alex James Chan, Mihaela van der Schaar

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Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning Enrico Marchesini, Davide Corsi, Alessandro Farinelli

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Geometry-Aware Gradient Algorithms for Neural Architecture Search Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar

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Geometry-Aware Instance-Reweighted Adversarial Training Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli

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Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

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Global Convergence of Three-Layer Neural Networks in the Mean Field Regime Huy Tuan Pham, Phan-Minh Nguyen

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Global Optimality of SoftMax Policy Gradient with Single Hidden Layer Neural Networks in the Mean-Field Regime Andrea Agazzi, Jianfeng Lu

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Go with the Flow: Adaptive Control for Neural ODEs Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen, Thomas Serre

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Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability Jeremy Cohen, Simran Kaur, Yuanzhi Li, J Zico Kolter, Ameet Talwalkar

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Gradient Origin Networks Sam Bond-Taylor, Chris G. Willcocks

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Gradient Projection Memory for Continual Learning Gobinda Saha, Isha Garg, Kaushik Roy

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Gradient Vaccine: Investigating and Improving Multi-Task Optimization in Massively Multilingual Models Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao

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gradSim: Differentiable Simulation for System Identification and Visuomotor Control J. Krishna Murthy, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jérôme Parent-Lévesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler

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Graph Coarsening with Neural Networks Chen Cai, Dingkang Wang, Yusu Wang

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Graph Convolution with Low-Rank Learnable Local Filters Xiuyuan Cheng, Zichen Miao, Qiang Qiu

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Graph Edit Networks Benjamin Paassen, Daniele Grattarola, Daniele Zambon, Cesare Alippi, Barbara Hammer

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Graph Information Bottleneck for Subgraph Recognition Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He

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Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan

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Graph-Based Continual Learning Binh Tang, David S. Matteson

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GraphCodeBERT: Pre-Training Code Representations with Data Flow Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

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GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, Caiming Xiong

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Greedy-GQ with Variance Reduction: Finite-Time Analysis and Improved Complexity Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou

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Grounded Language Learning Fast and Slow Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, Stephen Clark

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Grounding Language to Autonomously-Acquired Skills via Goal Generation Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, Olivier Sigaud

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Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee Kenneth Wong, Joshua B. Tenenbaum, Chuang Gan

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Group Equivariant Conditional Neural Processes Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo

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Group Equivariant Generative Adversarial Networks Neel Dey, Antong Chen, Soheil Ghafurian

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Group Equivariant Stand-Alone Self-Attention for Vision David W. Romero, Jean-Baptiste Cordonnier

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Growing Efficient Deep Networks by Structured Continuous Sparsification Xin Yuan, Pedro Henrique Pamplona Savarese, Michael Maire

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GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen

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HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents Deyao Zhu, Mohamed Zahran, Li Erran Li, Mohamed Elhoseiny

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Heating up Decision Boundaries: Isocapacitory Saturation, Adversarial Scenarios and Generalization Bounds Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee

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HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Enmao Diao, Jie Ding, Vahid Tarokh

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Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

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Hierarchical Autoregressive Modeling for Neural Video Compression Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

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Hierarchical Reinforcement Learning by Discovering Intrinsic Options Jesse Zhang, Haonan Yu, Wei Xu

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High-Capacity Expert Binary Networks Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos

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Hopfield Networks Is All You Need Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David Kreil, Michael K Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter

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Hopper: Multi-Hop Transformer for Spatiotemporal Reasoning Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf

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How Benign Is Benign Overfitting ? Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip Torr

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How Does Mixup Help with Robustness and Generalization? Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou

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How Much Over-Parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu

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How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-Ichi Kawarabayashi, Stefanie Jegelka

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How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision Dongkwan Kim, Alice Oh

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Human-Level Performance in No-Press Diplomacy via Equilibrium Search Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown

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HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark Chaojian Li, Zhongzhi Yu, Yonggan Fu, Yongan Zhang, Yang Zhao, Haoran You, Qixuan Yu, Yue Wang, Cong Hao, Yingyan Lin

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Hyperbolic Neural Networks++ Ryohei Shimizu, Yusuke Mukuta, Tatsuya Harada

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HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks Zhou Xian, Shamit Lal, Hsiao-Yu Tung, Emmanouil Antonios Platanios, Katerina Fragkiadaki

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HyperGrid Transformers: Towards a Single Model for Multiple Tasks Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan

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Identifying Nonlinear Dynamical Systems with Multiple Time Scales and Long-Range Dependencies Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz

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Identifying Physical Law of Hamiltonian Systems via Meta-Learning Seungjun Lee, Haesang Yang, Woojae Seong

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IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans

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IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning Manli Zhang, Jianhong Zhang, Zhiwu Lu, Tao Xiang, Mingyu Ding, Songfang Huang

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Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels Denis Yarats, Ilya Kostrikov, Rob Fergus

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Image GANs Meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler

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Impact of Representation Learning in Linear Bandits Jiaqi Yang, Wei Hu, Jason D. Lee, Simon Shaolei Du

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Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time Tolga Ergen, Mert Pilanci

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Implicit Gradient Regularization David Barrett, Benoit Dherin

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Implicit Normalizing Flows Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu

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Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

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Improve Object Detection with Feature-Based Knowledge Distillation: Towards Accurate and Efficient Detectors Linfeng Zhang, Kaisheng Ma

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Improved Autoregressive Modeling with Distribution Smoothing Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon

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Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces Jack Prescott, Xiao Zhang, David Evans

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Improving Adversarial Robustness via Channel-Wise Activation Suppressing Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang

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Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui

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Improving Transformation Invariance in Contrastive Representation Learning Adam Foster, Rattana Pukdee, Tom Rainforth

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Improving VAEs' Robustness to Adversarial Attack Matthew JF Willetts, Alexander Camuto, Tom Rainforth, S Roberts, Christopher C Holmes

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Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning Siyang Yuan, Pengyu Cheng, Ruiyi Zhang, Weituo Hao, Zhe Gan, Lawrence Carin

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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-Label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah

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In Search of Lost Domain Generalization Ishaan Gulrajani, David Lopez-Paz

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In-N-Out: Pre-Training and Self-Training Using Auxiliary Information for Out-of-Distribution Robustness Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang

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Incorporating Symmetry into Deep Dynamics Models for Improved Generalization Rui Wang, Robin Walters, Rose Yu

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Incremental Few-Shot Learning via Vector Quantization in Deep Embedded Space Kuilin Chen, Chi-Guhn Lee

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Individually Fair Gradient Boosting Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun

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Individually Fair Rankings Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun

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Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, Pan Li

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Influence Estimation for Generative Adversarial Networks Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru

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Influence Functions in Deep Learning Are Fragile Samyadeep Basu, Phil Pope, Soheil Feizi

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InfoBERT: Improving Robustness of Language Models from an Information Theoretic Perspective Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu

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Information Laundering for Model Privacy Xinran Wang, Yu Xiang, Jun Gao, Jie Ding

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Initialization and Regularization of Factorized Neural Layers Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolo Fusi

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INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving Yuhuai Wu, Albert Jiang, Jimmy Ba, Roger Baker Grosse

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Integrating Categorical Semantics into Unsupervised Domain Translation Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron Courville

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Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

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Interpretable Models for Granger Causality Using Self-Explaining Neural Networks Ričards Marcinkevičs, Julia E Vogt

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Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne

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Interpreting and Boosting Dropout from a Game-Theoretic View Hao Zhang, Sen Li, YinChao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang

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Interpreting Graph Neural Networks for NLP with Differentiable Edge Masking Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov

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Interpreting Knowledge Graph Relation Representation from Word Embeddings Carl Allen, Ivana Balazevic, Timothy Hospedales

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Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds Efthymios Tzinis, Scott Wisdom, Aren Jansen, Shawn Hershey, Tal Remez, Dan Ellis, John R. Hershey

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Intraclass Clustering: An Implicit Learning Ability That Regularizes DNNs Simon Carbonnelle, Christophe De Vleeschouwer

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Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlikova, Michael Krone, Tobias Ritschel, Timo Ropinski

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IOT: Instance-Wise Layer Reordering for Transformer Structures Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

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Is Attention Better than Matrix Decomposition? Zhengyang Geng, Meng-Hao Guo, Hongxu Chen, Xia Li, Ke Wei, Zhouchen Lin

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Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study Zhiqiang Shen, Zechun Liu, Dejia Xu, Zitian Chen, Kwang-Ting Cheng, Marios Savvides

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IsarStep: A Benchmark for High-Level Mathematical Reasoning Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson

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Isometric Propagation Network for Generalized Zero-Shot Learning Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang

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Isometric Transformation Invariant and Equivariant Graph Convolutional Networks Masanobu Horie, Naoki Morita, Toshiaki Hishinuma, Yu Ihara, Naoto Mitsume

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Isotropy in the Contextual Embedding Space: Clusters and Manifolds Xingyu Cai, Jiaji Huang, Yuchen Bian, Kenneth Church

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Iterated Learning for Emergent Systematicity in VQA Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

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Iterative Empirical Game Solving via Single Policy Best Response Max Smith, Thomas Anthony, Michael Wellman

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Kanerva++: Extending the Kanerva Machine with Differentiable, Locally Block Allocated Latent Memory Jason Ramapuram, Yan Wu, Alexandros Kalousis

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Knowledge Distillation as Semiparametric Inference Tri Dao, Govinda M Kamath, Vasilis Syrgkanis, Lester Mackey

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Knowledge Distillation via SoftMax Regression Representation Learning Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos

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LambdaNetworks: Modeling Long-Range Interactions Without Attention Irwan Bello

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Language-Agnostic Representation Learning of Source Code from Structure and Context Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann

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Large Associative Memory Problem in Neurobiology and Machine Learning Dmitry Krotov, John J. Hopfield

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Large Batch Simulation for Deep Reinforcement Learning Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian

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Large Scale Image Completion via Co-Modulated Generative Adversarial Networks Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I-Chao Chang, Yan Xu

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Large-Width Functional Asymptotics for Deep Gaussian Neural Networks Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti

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Latent Convergent Cross Mapping Edward De Brouwer, Adam Arany, Jaak Simm, Yves Moreau

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Latent Skill Planning for Exploration and Transfer Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

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Layer-Adaptive Sparsity for the Magnitude-Based Pruning Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin

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LEAF: A Learnable Frontend for Audio Classification Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi

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Learnable Embedding Sizes for Recommender Systems Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, Yong Li

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Learning "What-If" Explanations for Sequential Decision-Making Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

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Learning a Latent Search Space for Routing Problems Using Variational Autoencoders André Hottung, Bhanu Bhandari, Kevin Tierney

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Learning a Latent Simplex in Input Sparsity Time Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David Woodruff, Samson Zhou

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Learning a Minimax Optimizer: A Pilot Study Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang

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Learning Accurate Entropy Model with Global Reference for Image Compression Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Li Hao, Rong Jin

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Learning Advanced Mathematical Computations from Examples Francois Charton, Amaury Hayat, Guillaume Lample

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Learning and Evaluating Representations for Deep One-Class Classification Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister

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Learning Associative Inference Using Fast Weight Memory Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber

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Learning Better Structured Representations Using Low-Rank Adaptive Label Smoothing Asish Ghoshal, Xilun Chen, Sonal Gupta, Luke Zettlemoyer, Yashar Mehdad

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Learning Continuous-Time PDEs from Sparse Data with Graph Neural Networks Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

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Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency Qiang Zhang, Tete Xiao, Alexei A Efros, Lerrel Pinto, Xiaolong Wang

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Learning Deep Features in Instrumental Variable Regression Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

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Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling Yang Zhao, Jianwen Xie, Ping Li

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Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma

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Learning Explanations That Are Hard to Vary Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf

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Learning from Demonstration with Weakly Supervised Disentanglement Yordan Hristov, Subramanian Ramamoorthy

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Learning from Others' Mistakes: Avoiding Dataset Biases Without Modeling Them Victor Sanh, Thomas Wolf, Yonatan Belinkov, Alexander M Rush

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Learning from Protein Structure with Geometric Vector Perceptrons Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, Ron Dror

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Learning Generalizable Visual Representations via Interactive Gameplay Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi

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Learning Hyperbolic Representations of Topological Features Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan

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Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models That Generalize Nils Wandel, Michael Weinmann, Reinhard Klein

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Learning Invariant Representations for Reinforcement Learning Without Reconstruction Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine

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Learning Long-Term Visual Dynamics with Region Proposal Interaction Networks Haozhi Qi, Xiaolong Wang, Deepak Pathak, Yi Ma, Jitendra Malik

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Learning Manifold Patch-Based Representations of Man-Made Shapes Dmitriy Smirnov, Mikhail Bessmeltsev, Justin Solomon

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Learning Mesh-Based Simulation with Graph Networks Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter Battaglia

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Learning N:M Fine-Grained Structured Sparse Neural Networks from Scratch Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, Hongsheng Li

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Learning Neural Event Functions for Ordinary Differential Equations Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

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Learning Neural Generative Dynamics for Molecular Conformation Generation Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang

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Learning Parametrised Graph Shift Operators George Dasoulas, Johannes F. Lutzeyer, Michalis Vazirgiannis

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Learning Perturbation Sets for Robust Machine Learning Eric Wong, J Zico Kolter

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Learning Reasoning Paths over Semantic Graphs for Video-Grounded Dialogues Hung Le, Nancy F. Chen, Steven Hoi

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Learning Robust State Abstractions for Hidden-Parameter Block MDPs Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

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Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan

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Learning Structural Edits via Incremental Tree Transformations Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, Graham Neubig

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Learning Subgoal Representations with Slow Dynamics Siyuan Li, Lulu Zheng, Jianhao Wang, Chongjie Zhang

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Learning Task Decomposition with Ordered Memory Policy Network Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

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Learning Task-General Representations with Generative Neuro-Symbolic Modeling Reuben Feinman, Brenden M. Lake

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Learning the Pareto Front with Hypernetworks Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik

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Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation Mrigank Raman, Aaron Chan, Siddhant Agarwal, PeiFeng Wang, Hansen Wang, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren

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Learning to Generate 3D Shapes with Generative Cellular Automata Dongsu Zhang, Changwoon Choi, Jeonghwan Kim, Young Min Kim

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Learning to Live with Dale's Principle: ANNs with Separate Excitatory and Inhibitory Units Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette, Dimitri Michael Kullmann, Blake Aaron Richards

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Learning to Make Decisions via Submodular Regularization Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen

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Learning to Reach Goals via Iterated Supervised Learning Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine

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Learning to Recombine and Resample Data for Compositional Generalization Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas

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Learning to Represent Action Values as a Hypergraph on the Action Vertices Arash Tavakoli, Mehdi Fatemi, Petar Kormushev

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Learning to Sample with Local and Global Contexts in Experience Replay Buffer Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, Sung Ju Hwang

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Learning to Set Waypoints for Audio-Visual Navigation Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh Kumar Ramakrishnan, Kristen Grauman

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Learning Value Functions in Deep Policy Gradients Using Residual Variance Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux

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Learning What to Do by Simulating the past David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan

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Learning with AMIGo: Adversarially Motivated Intrinsic Goals Andres Campero, Roberta Raileanu, Heinrich Kuttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette

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Learning with Feature-Dependent Label Noise: A Progressive Approach Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen

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Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, Yang Liu

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Learning-Based Support Estimation in Sublinear Time Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner

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Lifelong Learning of Compositional Structures Jorge A Mendez, Eric Eaton

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LiftPool: Bidirectional ConvNet Pooling Jiaojiao Zhao, Cees G. M. Snoek

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Linear Convergent Decentralized Optimization with Compression Xiaorui Liu, Yao Li, Rongrong Wang, Jiliang Tang, Ming Yan

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Linear Last-Iterate Convergence in Constrained Saddle-Point Optimization Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo

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Linear Mode Connectivity in Multitask and Continual Learning Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu, Hassan Ghasemzadeh

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Lipschitz Recurrent Neural Networks N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

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Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation Tanner Fiez, Lillian J Ratliff

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Local Search Algorithms for Rank-Constrained Convex Optimization Kyriakos Axiotis, Maxim Sviridenko

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Locally Free Weight Sharing for Network Width Search Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu

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Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang

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Long Range Arena : A Benchmark for Efficient Transformers Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler

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Long-Tail Learning via Logit Adjustment Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

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Long-Tailed Recognition by Routing Diverse Distribution-Aware Experts Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella Yu

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Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, Zhenguo Li

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Lossless Compression of Structured Convolutional Models via Lifting Gustav Sourek, Filip Zelezny, Ondrej Kuzelka

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LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P Dickerson, Gavin Taylor, Tom Goldstein

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MALI: A Memory Efficient and Reverse Accurate Integrator for Neural ODEs Juntang Zhuang, Nicha C Dvornek, Sekhar Tatikonda, James s Duncan

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Mapping the Timescale Organization of Neural Language Models Hsiang-Yun Sherry Chien, Jinhan Zhang, Christopher Honey

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MARS: Markov Molecular Sampling for Multi-Objective Drug Discovery Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, Lei Li

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Mastering Atari with Discrete World Models Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, Jimmy Ba

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Mathematical Reasoning via Self-Supervised Skip-Tree Training Markus Norman Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

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Measuring Massive Multitask Language Understanding Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt

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MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning Nanyi Fei, Zhiwu Lu, Tao Xiang, Songfang Huang

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Memory Optimization for Deep Networks Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Kraehenbuehl

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Meta Back-Translation Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig

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Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning Dong Bok Lee, Dongchan Min, Seanie Lee, Sung Ju Hwang

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Meta-Learning of Structured Task Distributions in Humans and Machines Sreejan Kumar, Ishita Dasgupta, Jonathan Cohen, Nathaniel Daw, Thomas Griffiths

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Meta-Learning Symmetries by Reparameterization Allan Zhou, Tom Knowles, Chelsea Finn

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Meta-Learning with Negative Learning Rates Alberto Bernacchia

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Meta-Learning with Neural Tangent Kernels Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, Jinhui Xu

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MetaNorm: Learning to Normalize Few-Shot Batches Across Domains Yingjun Du, Xiantong Zhen, Ling Shao, Cees G. M. Snoek

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MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering Tsung Wei Tsai, Chongxuan Li, Jun Zhu

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Mind the Gap When Conditioning Amortised Inference in Sequential Latent-Variable Models Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt

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Mind the Pad -- CNNs Can Develop Blind Spots Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Jun Yuan, Orion Reblitz-Richardson

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Minimum Width for Universal Approximation Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin

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Mirostat: A Neural Text Decoding Algorithm That Directly Controls Perplexity Sourya Basu, Govardana Sachitanandam Ramachandran, Nitish Shirish Keskar, Lav R. Varshney

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Mixed-Features Vectors and Subspace Splitting Alejandro Pimentel-Alarcón, Daniel L. Pimentel-Alarcón

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MixKD: Towards Efficient Distillation of Large-Scale Language Models Kevin J Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, Lawrence Carin

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MODALS: Modality-Agnostic Automated Data Augmentation in the Latent Space Tsz-Him Cheung, Dit-Yan Yeung

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Model Patching: Closing the Subgroup Performance Gap with Data Augmentation Karan Goel, Albert Gu, Yixuan Li, Christopher Re

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Model-Based Micro-Data Reinforcement Learning: What Are the Crucial Model Properties and Which Model to Choose? Balázs Kégl, Gabriel Hurtado, Albert Thomas

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Model-Based Offline Planning Arthur Argenson, Gabriel Dulac-Arnold

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Model-Based Visual Planning with Self-Supervised Functional Distances Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

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Modeling the Second Player in Distributionally Robust Optimization Paul Michel, Tatsunori Hashimoto, Graham Neubig

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Modelling Hierarchical Structure Between Dialogue Policy and Natural Language Generator with Option Framework for Task-Oriented Dialogue System Jianhong Wang, Yuan Zhang, Tae-Kyun Kim, Yunjie Gu

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Molecule Optimization by Explainable Evolution Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

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MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Re

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Monotonic Kronecker-Factored Lattice William Taylor Bakst, Nobuyuki Morioka, Erez Louidor

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Monte-Carlo Planning and Learning with Language Action Value Estimates Youngsoo Jang, Seokin Seo, Jongmin Lee, Kee-Eung Kim

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MoPro: Webly Supervised Learning with Momentum Prototypes Junnan Li, Caiming Xiong, Steven Hoi

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More or Less: When and How to Build Convolutional Neural Network Ensembles Abdul Wasay, Stratos Idreos

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MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond Duy Kien Nguyen, Vedanuj Goswami, Xinlei Chen

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Multi-Class Uncertainty Calibration via Mutual Information Maximization-Based Binning Kanil Patel, William H. Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang

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Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks Timothy Castiglia, Anirban Das, Stacy Patterson

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Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning a Randomly Weighted Network James Diffenderfer, Bhavya Kailkhura

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Multi-Resolution Modeling of a Discrete Stochastic Process Identifies Causes of Cancer Adam Uri Yaari, Maxwell Sherman, Oliver Clarke Priebe, Po-Ru Loh, Boris Katz, Andrei Barbu, Bonnie Berger

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Multi-Time Attention Networks for Irregularly Sampled Time Series Satya Narayan Shukla, Benjamin Marlin

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Multi-Timescale Representation Learning in LSTM Language Models Shivangi Mahto, Vy Ai Vo, Javier S. Turek, Alexander Huth

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MultiModalQA: Complex Question Answering over Text, Tables and Images Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, Jonathan Berant

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Multiplicative Filter Networks Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter

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Multiscale Score Matching for Out-of-Distribution Detection Ahsan Mahmood, Junier Oliva, Martin Andreas Styner

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Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M Bergmann, Roland Vollgraf

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Mutual Information State Intrinsic Control Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu

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My Body Is a Cage: The Role of Morphology in Graph-Based Incompatible Control Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson

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NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition Abhinav Mehrotra, Alberto Gil C. P. Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla, Thomas Chau, Mohamed S Abdelfattah, Samin Ishtiaq, Nicholas Donald Lane

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NBDT: Neural-Backed Decision Tree Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

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Nearest Neighbor Machine Translation Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis

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Negative Data Augmentation Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon

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NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation Angtian Wang, Adam Kortylewski, Alan Yuille

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Net-DNF: Effective Deep Modeling of Tabular Data Liran Katzir, Gal Elidan, Ran El-Yaniv

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Network Pruning That Matters: A Case Study on Retraining Variants Duong Hoang Le, Binh-Son Hua

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Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

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Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective Wuyang Chen, Xinyu Gong, Zhangyang Wang

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Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma

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Neural Delay Differential Equations Qunxi Zhu, Yao Guo, Wei Lin

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Neural Gradients Are Near-Lognormal: Improved Quantized and Sparse Training Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry

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Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering Calypso Herrera, Florian Krach, Josef Teichmann

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Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces Yatin Nandwani, Deepanshu Jindal, Mausam, Parag Singla

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Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics Daniel Kunin, Javier Sagastuy-Brena, Surya Ganguli, Daniel LK Yamins, Hidenori Tanaka

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Neural Networks for Learning Counterfactual G-Invariances from Single Environments S Chandra Mouli, Bruno Ribeiro

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Neural Networks with Late-Phase Weights Johannes von Oswald, Seijin Kobayashi, Joao Sacramento, Alexander Meulemans, Christian Henning, Benjamin F Grewe

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Neural ODE Processes Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

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Neural Pruning via Growing Regularization Huan Wang, Can Qin, Yulun Zhang, Yun Fu

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Neural Representation and Generation for RNA Secondary Structures Zichao Yan, William L. Hamilton, Mathieu Blanchette

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Neural Spatio-Temporal Point Processes Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

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Neural Synthesis of Binaural Speech from Mono Audio Alexander Richard, Dejan Markovic, Israel D. Gebru, Steven Krenn, Gladstone Alexander Butler, Fernando Torre, Yaser Sheikh

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Neural Thompson Sampling Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu

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Neural Topic Model via Optimal Transport He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

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Neurally Augmented ALISTA Freya Behrens, Jonathan Sauder, Peter Jung

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New Bounds for Distributed Mean Estimation and Variance Reduction Peter Davies, Vijaykrishna Gurunanthan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh

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No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi

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No MCMC for Me: Amortized Sampling for Fast and Stable Training of Energy-Based Models Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

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Noise Against Noise: Stochastic Label Noise Helps Combat Inherent Label Noise Pengfei Chen, Guangyong Chen, Junjie Ye, Jingwei Zhao, Pheng-Ann Heng

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Noise or Signal: The Role of Image Backgrounds in Object Recognition Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry

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Non-Asymptotic Confidence Intervals of Off-Policy Evaluation: Primal and Dual Bounds Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu

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Nonseparable Symplectic Neural Networks Shiying Xiong, Yunjin Tong, Xingzhe He, Shuqi Yang, Cheng Yang, Bo Zhu

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Not-MIWAE: Deep Generative Modelling with Missing Not at Random Data Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

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NOVAS: Non-Convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control Ioannis Exarchos, Marcus Aloysius Pereira, Ziyi Wang, Evangelos Theodorou

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Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov

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Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation Justin Fu, Sergey Levine

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On Data-Augmentation and Consistency-Based Semi-Supervised Learning Atin Ghosh, Alexandre H. Thiery

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On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu

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On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang

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On Graph Neural Networks Versus Graph-Augmented MLPs Lei Chen, Zhengdao Chen, Joan Bruna

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On InstaHide, Phase Retrieval, and Sparse Matrix Factorization Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

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On Learning Universal Representations Across Languages Xiangpeng Wei, Rongxiang Weng, Yue Hu, Luxi Xing, Heng Yu, Weihua Luo

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On Position Embeddings in BERT Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen

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On Self-Supervised Image Representations for GAN Evaluation Stanislav Morozov, Andrey Voynov, Artem Babenko

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On Statistical Bias in Active Learning: How and When to Fix It Sebastian Farquhar, Yarin Gal, Tom Rainforth

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On the Bottleneck of Graph Neural Networks and Its Practical Implications Uri Alon, Eran Yahav

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On the Critical Role of Conventions in Adaptive Human-AI Collaboration Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh

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On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis Zhong Li, Jiequn Han, Weinan E, Qianxiao Li

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On the Dynamics of Training Attention Models Haoye Lu, Yongyi Mao, Amiya Nayak

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On the Geometry of Generalization and Memorization in Deep Neural Networks Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung

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On the Impossibility of Global Convergence in Multi-Loss Optimization Alistair Letcher

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On the Mapping Between Hopfield Networks and Restricted Boltzmann Machines Matthew Smart, Anton Zilman

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On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel L Smith, Benoit Dherin, David Barrett, Soham De

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On the Role of Planning in Model-Based Deep Reinforcement Learning Jessica B Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Veličković, Theophane Weber

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On the Stability of Fine-Tuning BERT: Misconceptions, Explanations, and Strong Baselines Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow

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On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers Kenji Kawaguchi

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On the Transfer of Disentangled Representations in Realistic Settings Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

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On the Universality of Rotation Equivariant Point Cloud Networks Nadav Dym, Haggai Maron

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On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller

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One Network Fits All? Modular Versus Monolithic Task Formulations in Neural Networks Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang

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Online Adversarial Purification Based on Self-Supervised Learning Changhao Shi, Chester Holtz, Gal Mishne

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OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum

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Open Question Answering over Tables and Text Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William W. Cohen

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Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks Shikuang Deng, Shi Gu

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Optimal Rates for Averaged Stochastic Gradient Descent Under Neural Tangent Kernel Regime Atsushi Nitanda, Taiji Suzuki

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Optimal Regularization Can Mitigate Double Descent Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma

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Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy

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Optimizing Memory Placement Using Evolutionary Graph Reinforcement Learning Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar

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Orthogonalizing Convolutional Layers with the Cayley Transform Asher Trockman, J Zico Kolter

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Overfitting for Fun and Profit: Instance-Adaptive Data Compression Ties van Rozendaal, Iris AM Huijben, Taco Cohen

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Overparameterisation and Worst-Case Generalisation: Friend or Foe? Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

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PAC Confidence Predictions for Deep Neural Network Classifiers Sangdon Park, Shuo Li, Insup Lee, Osbert Bastani

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Parameter Efficient Multimodal Transformers for Video Representation Learning Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, Yale Song

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Parameter-Based Value Functions Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

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Parrot: Data-Driven Behavioral Priors for Reinforcement Learning Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine

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Partitioned Learned Bloom Filters Kapil Vaidya, Eric Knorr, Michael Mitzenmacher, Tim Kraska

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PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds Yujia Liu, Stefano D'Aronco, Konrad Schindler, Jan Dirk Wegner

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PDE-Driven Spatiotemporal Disentanglement Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari

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Perceptual Adversarial Robustness: Defense Against Unseen Threat Models Cassidy Laidlaw, Sahil Singla, Soheil Feizi

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Personalized Federated Learning with First Order Model Optimization Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, Jose M. Alvarez

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Physics-Aware, Probabilistic Model Order Reduction with Guaranteed Stability Sebastian Kaltenbach, Phaedon Stelios Koutsourelakis

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Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks Ingmar Schubert, Ozgur S Oguz, Marc Toussaint

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Planning from Pixels Using Inverse Dynamics Models Keiran Paster, Sheila A. McIlraith, Jimmy Ba

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PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan

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PMI-Masking: Principled Masking of Correlated Spans Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham

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PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection Wu Xiongwei, Doyen Sahoo, Steven Hoi

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Policy-Driven Attack: Learning to Query for Hard-Label Black-Box Adversarial Examples Ziang Yan, Yiwen Guo, Jian Liang, Changshui Zhang

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Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design Xiufeng Yang, Tanuj Aasawat, Kazuki Yoshizoe

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Practical Real Time Recurrent Learning with a Sparse Approximation Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves

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Pre-Training Text-to-Text Transformers for Concept-Centric Common Sense Wangchunshu Zhou, Dong-Ho Lee, Ravi Kiran Selvam, Seyeon Lee, Xiang Ren

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Predicting Classification Accuracy When Adding New Unobserved Classes Yuli Slavutsky, Yuval Benjamini

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Predicting Inductive Biases of Pre-Trained Models Charles Lovering, Rohan Jha, Tal Linzen, Ellie Pavlick

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Predicting Infectiousness for Proactive Contact Tracing Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David Buckeridge, Gaetan Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz Gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

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Prediction and Generalisation over Directed Actions by Grid Cells Changmin Yu, Timothy Behrens, Neil Burgess

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Primal Wasserstein Imitation Learning Robert Dadashi, Leonard Hussenot, Matthieu Geist, Olivier Pietquin

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Private Image Reconstruction from System Side Channels Using Generative Models Yuanyuan Yuan, Shuai Wang, Junping Zhang

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Private Post-GAN Boosting Marcel Neunhoeffer, Steven Wu, Cynthia Dwork

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Probabilistic Numeric Convolutional Neural Networks Marc Anton Finzi, Roberto Bondesan, Max Welling

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Probing BERT in Hyperbolic Spaces Boli Chen, Yao Fu, Guangwei Xu, Pengjun Xie, Chuanqi Tan, Mosha Chen, Liping Jing

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Progressive Skeletonization: Trimming More Fat from a Network at Initialization Pau de Jorge, Amartya Sanyal, Harkirat Behl, Philip Torr, Grégory Rogez, Puneet K. Dokania

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Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows Chris Cannella, Mohammadreza Soltani, Vahid Tarokh

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Property Controllable Variational Autoencoder via Invertible Mutual Dependence Xiaojie Guo, Yuanqi Du, Liang Zhao

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Protecting DNNs from Theft Using an Ensemble of Diverse Models Sanjay Kariyappa, Atul Prakash, Moinuddin K Qureshi

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Prototypical Contrastive Learning of Unsupervised Representations Junnan Li, Pan Zhou, Caiming Xiong, Steven Hoi

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Prototypical Representation Learning for Relation Extraction Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, Rui Zhang

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Provable Rich Observation Reinforcement Learning with Combinatorial Latent States Dipendra Misra, Qinghua Liu, Chi Jin, John Langford

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Provably Robust Classification of Adversarial Examples with Detection Fatemeh Sheikholeslami, Ali Lotfi, J Zico Kolter

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Proximal Gradient Descent-Ascent: Variable Convergence Under KŁ Geometry Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang

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Pruning Neural Networks at Initialization: Why Are We Missing the Mark? Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

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PseudoSeg: Designing Pseudo Labels for Semantic Segmentation Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister

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PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang, Mohan Kankanhalli

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QPLEX: Duplex Dueling Multi-Agent Q-Learning Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, Chongjie Zhang

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Quantifying Differences in Reward Functions Adam Gleave, Michael D Dennis, Shane Legg, Stuart Russell, Jan Leike

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R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu, Matthew B. Blaschko

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Random Feature Attention Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith, Lingpeng Kong

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Randomized Automatic Differentiation Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P Adams

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Randomized Ensembled Double Q-Learning: Learning Fast Without a Model Xinyue Chen, Che Wang, Zijian Zhou, Keith W. Ross

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Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu

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Rao-Blackwellizing the Straight-Through Gumbel-SoftMax Gradient Estimator Max B Paulus, Chris J. Maddison, Andreas Krause

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Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets Hayeon Lee, Eunyoung Hyung, Sung Ju Hwang

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Rapid Task-Solving in Novel Environments Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew Botvinick, David Raposo

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Recurrent Independent Mechanisms Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf

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Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks Thomas Bird, Friso Kingma, David Barber

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Refining Deep Generative Models via Discriminator Gradient Flow Abdul Fatir Ansari, Ming Liang Ang, Harold Soh

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Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell

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Regularized Inverse Reinforcement Learning Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau

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Reinforcement Learning with Random Delays Yann Bouteiller, Simon Ramstedt, Giovanni Beltrame, Christopher Pal, Jonathan Binas

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Relating by Contrasting: A Data-Efficient Framework for Multimodal Generative Models Yuge Shi, Brooks Paige, Philip Torr, Siddharth N

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Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell

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Removing Undesirable Feature Contributions Using Out-of-Distribution Data Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon

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Representation Balancing Offline Model-Based Reinforcement Learning Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim

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Representation Learning for Improved Interpretability and Classification Accuracy of Clinical Factors from EEG Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, Greg Hajcak

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Representation Learning for Sequence Data with Deep Autoencoding Predictive Components Junwen Bai, Weiran Wang, Yingbo Zhou, Caiming Xiong

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Representation Learning via Invariant Causal Mechanisms Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, Charles Blundell

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Representing Partial Programs with Blended Abstract Semantics Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

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Repurposing Pretrained Models for Robust Out-of-Domain Few-Shot Learning Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien

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Reset-Free Lifelong Learning with Skill-Space Planning Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch

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ResNet After All: Neural ODEs and Their Numerical Solution Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

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Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh

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Rethinking Attention with Performers Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, Adrian Weller

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Rethinking Embedding Coupling in Pre-Trained Language Models Hyung Won Chung, Thibault Fevry, Henry Tsai, Melvin Johnson, Sebastian Ruder

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Rethinking Positional Encoding in Language Pre-Training Guolin Ke, Di He, Tie-Yan Liu

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Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, Qian Zhang

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Rethinking the Role of Gradient-Based Attribution Methods for Model Interpretability Suraj Srinivas, Francois Fleuret

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Retrieval-Augmented Generation for Code Summarization via Hybrid GNN Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, Yang Liu

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Return-Based Contrastive Representation Learning for Reinforcement Learning Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Li Jian, Nenghai Yu, Tie-Yan Liu

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Revisiting Dynamic Convolution via Matrix Decomposition Yunsheng Li, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Ye Yu, Lu Yuan, Zicheng Liu, Mei Chen, Nuno Vasconcelos

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Revisiting Few-Sample BERT Fine-Tuning Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, Yoav Artzi

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Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas Huang, Hyungsuk Yoon, Honglak Lee, Seunghoon Hong

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Revisiting Locally Supervised Learning: An Alternative to End-to-End Training Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, Gao Huang

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Reweighting Augmented Samples by Minimizing the Maximal Expected Loss Mingyang Yi, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu, Zhi-Ming Ma

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Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks Christian H.X. Ali Mehmeti-Göpel, David Hartmann, Michael Wand

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Risk-Averse Offline Reinforcement Learning Núria Armengol Urpí, Sebastian Curi, Andreas Krause

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RMSProp Converges with Proper Hyper-Parameter Naichen Shi, Dawei Li, Mingyi Hong, Ruoyu Sun

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RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang

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Robust and Generalizable Visual Representation Learning via Random Convolutions Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, Marc Niethammer

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Robust Curriculum Learning: From Clean Label Detection to Noisy Label Self-Correction Tianyi Zhou, Shengjie Wang, Jeff Bilmes

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Robust Early-Learning: Hindering the Memorization of Noisy Labels Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang

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Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time Yu Cheng, Honghao Lin

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Robust Overfitting May Be Mitigated by Properly Learned Smoothening Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang

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Robust Pruning at Initialization Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh

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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary Huan Zhang, Hongge Chen, Duane S Boning, Cho-Jui Hsieh

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RODE: Learning Roles to Decompose Multi-Agent Tasks Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang

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SAFENet: A Secure, Accurate and Fast Neural Network Inference Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang

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SALD: Sign Agnostic Learning with Derivatives Matan Atzmon, Yaron Lipman

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Saliency Is a Possible Red Herring When Diagnosing Poor Generalization Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen

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SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization A F M Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae

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Sample-Efficient Automated Deep Reinforcement Learning Jörg K.H. Franke, Gregor Koehler, André Biedenkapp, Frank Hutter

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Scalable Bayesian Inverse Reinforcement Learning Alex James Chan, Mihaela van der Schaar

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Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

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Scalable Transfer Learning with Expert Models Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby

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Scaling Symbolic Methods Using Gradients for Neural Model Explanation Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

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Scaling the Convex Barrier with Active Sets Alessandro De Palma, Harkirat Behl, Rudy R Bunel, Philip Torr, M. Pawan Kumar

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Score-Based Generative Modeling Through Stochastic Differential Equations Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

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SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing Tao Yu, Rui Zhang, Alex Polozov, Christopher Meek, Ahmed Hassan Awadallah

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SEDONA: Search for Decoupled Neural Networks Toward Greedy Block-Wise Learning Myeongjang Pyeon, Jihwan Moon, Taeyoung Hahn, Gunhee Kim

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SEED: Self-Supervised Distillation for Visual Representation Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang, Zicheng Liu

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Selective Classification Can Magnify Disparities Across Groups Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, Percy Liang

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Selectivity Considered Harmful: Evaluating the Causal Impact of Class Selectivity in DNNs Matthew L Leavitt, Ari S. Morcos

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Self-Supervised Adversarial Robustness for the Low-Label, High-Data Regime Sven Gowal, Po-Sen Huang, Aaron van den Oord, Timothy Mann, Pushmeet Kohli

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Self-Supervised Learning from a Multi-View Perspective Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency

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Self-Supervised Learning of Compressed Video Representations Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song

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Self-Supervised Policy Adaptation During Deployment Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A Efros, Lerrel Pinto, Xiaolong Wang

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Self-Supervised Representation Learning with Relative Predictive Coding Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang, Han Zhao, Louis-Philippe Morency, Ruslan Salakhutdinov

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Self-Supervised Visual Reinforcement Learning with Object-Centric Representations Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius

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Self-Training for Few-Shot Transfer Across Extreme Task Differences Cheng Perng Phoo, Bharath Hariharan

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Semantic Re-Tuning with Contrastive Tension Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, Magnus Sahlgren

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Semi-Supervised Keypoint Localization Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh

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SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness Mikhail Yurochkin, Yuekai Sun

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Separation and Concentration in Deep Networks John Zarka, Florentin Guth, Stéphane Mallat

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Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections Csaba Toth, Patric Bonnier, Harald Oberhauser

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Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy Akinori F Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka

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Set Prediction Without Imposing Structure as Conditional Density Estimation David W Zhang, Gertjan J. Burghouts, Cees G. M. Snoek

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Shape or Texture: Understanding Discriminative Features in CNNs Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Björn Ommer, Konstantinos G. Derpanis, Neil Bruce

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Shape-Texture Debiased Neural Network Training Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

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Shapley Explainability on the Data Manifold Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, Ilya Feige

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Shapley Explanation Networks Rui Wang, Xiaoqian Wang, David I. Inouye

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Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation Biao Zhang, Ankur Bapna, Rico Sennrich, Orhan Firat

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Sharper Generalization Bounds for Learning with Gradient-Dominated Objective Functions Yunwen Lei, Yiming Ying

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Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

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Signatory: Differentiable Computations of the Signature and Logsignature Transforms, on Both CPU and GPU Patrick Kidger, Terry Lyons

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Simple Augmentation Goes a Long Way: ADRL for DNN Quantization Lin Ning, Guoyang Chen, Weifeng Zhang, Xipeng Shen

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Simple Spectral Graph Convolution Hao Zhu, Piotr Koniusz

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Single-Photon Image Classification Thomas Fischbacher, Luciano Sbaiz

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Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy Zuyue Fu, Zhuoran Yang, Zhaoran Wang

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SkipW: Resource Adaptable RNN with Strict Upper Computational Limit Tsiry Mayet, Anne Lambert, Pascal Leguyadec, Francoise Le Bolzer, François Schnitzler

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Sliced Kernelized Stein Discrepancy Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

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SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

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SOLAR: Sparse Orthogonal Learned and Random Embeddings Tharun Medini, Beidi Chen, Anshumali Shrivastava

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Solving Compositional Reinforcement Learning Problems via Task Reduction Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu

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Sparse Encoding for More-Interpretable Feature-Selecting Representations in Probabilistic Matrix Factorization Joshua C Chang, Patrick Fletcher, Jungmin Han, Ted L Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson C Chow

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Sparse Quantized Spectral Clustering Zhenyu Liao, Romain Couillet, Michael W. Mahoney

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Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

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Spatially Structured Recurrent Modules Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

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Spatio-Temporal Graph Scattering Transform Chao Pan, Siheng Chen, Antonio Ortega

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SSD: A Unified Framework for Self-Supervised Outlier Detection Vikash Sehwag, Mung Chiang, Prateek Mittal

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Stabilized Medical Image Attacks Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

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Statistical Inference for Individual Fairness Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun

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Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models Mitch Hill, Jonathan Craig Mitchell, Song-Chun Zhu

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Structured Prediction as Translation Between Augmented Natural Languages Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, Stefano Soatto

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Supervised Contrastive Learning for Pre-Trained Language Model Fine-Tuning Beliz Gunel, Jingfei Du, Alexis Conneau, Veselin Stoyanov

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Support-Set Bottlenecks for Video-Text Representation Learning Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander G Hauptmann, Joao F. Henriques, Andrea Vedaldi

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Symmetry-Aware Actor-Critic for 3D Molecular Design Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato

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Systematic Generalisation with Group Invariant Predictions Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville

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Taking Notes on the Fly Helps Language Pre-Training Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu

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Taming GANs with Lookahead-Minmax Tatjana Chavdarova, Matteo Pagliardini, Sebastian U Stich, François Fleuret, Martin Jaggi

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Targeted Attack Against Deep Neural Networks via Flipping Limited Weight Bits Jiawang Bai, Baoyuan Wu, Yong Zhang, Yiming Li, Zhifeng Li, Shu-Tao Xia

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Task-Agnostic Morphology Evolution Donald Joseph Hejna Iii, Pieter Abbeel, Lerrel Pinto

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Teaching Temporal Logics to Neural Networks Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus Norman Rabe, Bernd Finkbeiner

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Teaching with Commentaries Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton

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Temporally-Extended Ε-Greedy Exploration Will Dabney, Georg Ostrovski, Andre Barreto

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Tent: Fully Test-Time Adaptation by Entropy Minimization Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell

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Text Generation by Learning from Demonstrations Richard Yuanzhe Pang, He He

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The Deep Bootstrap Framework: Good Online Learners Are Good Offline Generalizers Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

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The Geometry of Integration in Text Classification RNNs Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

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The Importance of Pessimism in Fixed-Dataset Policy Optimization Jacob Buckman, Carles Gelada, Marc G Bellemare

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The Inductive Bias of ReLU Networks on Orthogonally Separable Data Mary Phuong, Christoph H Lampert

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The Intrinsic Dimension of Images and Its Impact on Learning Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

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The Recurrent Neural Tangent Kernel Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard Baraniuk

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The Risks of Invariant Risk Minimization Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

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The Role of Disentanglement in Generalisation Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey Bowers

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The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-Ball Methods Wei Tao, Sheng Long, Gaowei Wu, Qing Tao

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The Traveling Observer Model: Multi-Task Learning Through Spatial Variable Embeddings Elliot Meyerson, Risto Miikkulainen

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The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon

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Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma

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Theoretical Bounds on Estimation Error for Meta-Learning James Lucas, Mengye Ren, Irene Raissa KAMENI Kameni, Toniann Pitassi, Richard Zemel

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Tilted Empirical Risk Minimization Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

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Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou, Roderick Murray-Smith

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Topology-Aware Segmentation Using Discrete Morse Theory Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen

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Towards Faster and Stabilized GAN Training for High-Fidelity Few-Shot Image Synthesis Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

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Towards Impartial Multi-Task Learning Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, Wayne Zhang

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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan Paiton

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Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning Zhiyuan Li, Yuping Luo, Kaifeng Lyu

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Towards Robust Neural Networks via Close-Loop Control Zhuotong Chen, Qianxiao Li, Zheng Zhang

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Towards Robustness Against Natural Language Word Substitutions Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu

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Tradeoffs in Data Augmentation: An Empirical Study Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, Ethan Dyer

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Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs Jonathan Frankle, David J. Schwab, Ari S. Morcos

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Training GANs with Stronger Augmentations via Contrastive Discriminator Jongheon Jeong, Jinwoo Shin

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Training Independent Subnetworks for Robust Prediction Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

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Training with Quantization Noise for Extreme Model Compression Pierre Stock, Angela Fan, Benjamin Graham, Edouard Grave, Rémi Gribonval, Herve Jegou, Armand Joulin

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Trajectory Prediction Using Equivariant Continuous Convolution Robin Walters, Jinxi Li, Rose Yu

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Transformer Protein Language Models Are Unsupervised Structure Learners Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives

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Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson

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TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks Martin Trimmel, Henning Petzka, Cristian Sminchisescu

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Trusted Multi-View Classification Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

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UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems Jiayi Shen, Haotao Wang, Shupeng Gui, Jianchao Tan, Zhangyang Wang, Ji Liu

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Unbiased Teacher for Semi-Supervised Object Detection Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda

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Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs Cheng Wang, Carolin Lawrence, Mathias Niepert

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Uncertainty Estimation in Autoregressive Structured Prediction Andrey Malinin, Mark Gales

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Uncertainty in Gradient Boosting via Ensembles Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko

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Uncertainty Sets for Image Classifiers Using Conformal Prediction Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, Jitendra Malik

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Uncertainty-Aware Active Learning for Optimal Bayesian Classifier Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

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Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Zhaopeng Tu

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Understanding and Improving Lexical Choice in Non-Autoregressive Translation Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, Zhaopeng Tu

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Understanding Over-Parameterization in Generative Adversarial Networks Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

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Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr, Martin Jaggi

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Understanding the Failure Modes of Out-of-Distribution Generalization Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur

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Understanding the Role of Importance Weighting for Deep Learning Da Xu, Yuting Ye, Chuanwei Ruan

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Undistillable: Making a Nasty Teacher That CANNOT Teach Students Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

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Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory Paulo Tabuada, Bahman Gharesifard

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Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning Tsung-Wei Ke, Jyh-Jing Hwang, Stella Yu

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Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang

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Unsupervised Audiovisual Synthesis via Exemplar Autoencoders Kangle Deng, Aayush Bansal, Deva Ramanan

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Unsupervised Discovery of 3D Physical Objects from Video Yilun Du, Kevin A. Smith, Tomer Ullman, Joshua B. Tenenbaum, Jiajun Wu

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Unsupervised Meta-Learning Through Latent-Space Interpolation in Generative Models Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Boloni

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Unsupervised Object Keypoint Learning Using Local Spatial Predictability Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber

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Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding Sana Tonekaboni, Danny Eytan, Anna Goldenberg

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UPDeT: Universal Multi-Agent RL via Policy Decoupling with Transformers Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang

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Usable Information and Evolution of Optimal Representations During Training Michael Kleinman, Alessandro Achille, Daksh Idnani, Jonathan Kao

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Using Latent Space Regression to Analyze and Leverage Compositionality in GANs Lucy Chai, Jonas Wulff, Phillip Isola

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VA-RED$^2$: Video Adaptive Redundancy Reduction Bowen Pan, Rameswar Panda, Camilo Luciano Fosco, Chung-Ching Lin, Alex J Andonian, Yue Meng, Kate Saenko, Aude Oliva, Rogerio Feris

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VAEBM: A Symbiosis Between Variational Autoencoders and Energy-Based Models Zhisheng Xiao, Karsten Kreis, Jan Kautz, Arash Vahdat

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Variational Information Bottleneck for Effective Low-Resource Fine-Tuning Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson

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Variational Intrinsic Control Revisited Taehwan Kwon

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Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer

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VCNet and Functional Targeted Regularization for Learning Causal Effects of Continuous Treatments Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

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Vector-Output ReLU Neural Network Problems Are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-Time Algorithms Arda Sahiner, Tolga Ergen, John M. Pauly, Mert Pilanci

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Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images Rewon Child

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Viewmaker Networks: Learning Views for Unsupervised Representation Learning Alex Tamkin, Mike Wu, Noah Goodman

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VTNet: Visual Transformer Network for Object Goal Navigation Heming Du, Xin Yu, Liang Zheng

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Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics Yanchao Sun, Da Huo, Furong Huang

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Wandering Within a World: Online Contextualized Few-Shot Learning Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard Zemel

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WaNet - Imperceptible Warping-Based Backdoor Attack Tuan Anh Nguyen, Anh Tuan Tran

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Wasserstein Embedding for Graph Learning Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann

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Wasserstein-2 Generative Networks Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev

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Watch-and-Help: A Challenge for Social Perception and Human-AI Collaboration Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B. Tenenbaum, Sanja Fidler, Antonio Torralba

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WaveGrad: Estimating Gradients for Waveform Generation Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, William Chan

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What Are the Statistical Limits of Offline RL with Linear Function Approximation? Ruosong Wang, Dean Foster, Sham M. Kakade

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What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions Kiana Ehsani, Daniel Gordon, Thomas Hai Dang Nguyen, Roozbeh Mottaghi, Ali Farhadi

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What Makes Instance Discrimination Good for Transfer Learning? Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau, Stephen Lin

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What Matters for On-Policy Deep Actor-Critic Methods? a Large-Scale Study Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

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What Should Not Be Contrastive in Contrastive Learning Tete Xiao, Xiaolong Wang, Alexei A Efros, Trevor Darrell

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What They Do When in Doubt: A Study of Inductive Biases in Seq2seq Learners Eugene Kharitonov, Rahma Chaabouni

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When Do Curricula Work? Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

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When Does Preconditioning Help or Hurt Generalization? Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

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When Optimizing $f$-Divergence Is Robust with Label Noise Jiaheng Wei, Yang Liu

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Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? Zhiyuan Li, Yi Zhang, Sanjeev Arora

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Why Resampling Outperforms Reweighting for Correcting Sampling Bias with Stochastic Gradients Jing An, Lexing Ying, Yuhua Zhu

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Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-Eung Kim

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Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping, Liam H Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

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WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic Renkun Ni, Hong-min Chu, Oscar Castaneda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

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X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca Dragan, Sergey Levine

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You Only Need Adversarial Supervision for Semantic Image Synthesis Edgar Schönfeld, Vadim Sushko, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva

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Zero-Cost Proxies for Lightweight NAS Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, Nicholas Donald Lane

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Zero-Shot Synthesis with Group-Supervised Learning Yunhao Ge, Sami Abu-El-Haija, Gan Xin, Laurent Itti

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