NeurIPS 2021

2,334 papers

Artistic Style Transfer with Internal-external Learning and Contrastive Learning

Haibo Chen, lei zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo

Characterizing possible failure modes in physics-informed neural networks

Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, Michael W. Mahoney

Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark

Stefan O'Toole, Nir Lipovetzky, Miquel Ramirez, Adrian Pearce

On the Algorithmic Stability of Adversarial Training

Yue Xing, Qifan Song, Guang Cheng

Generalized Linear Bandits with Local Differential Privacy

Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang

Pragmatic Image Compression for Human-in-the-Loop Decision-Making

Sid Reddy, Anca Dragan, Sergey Levine

Online false discovery rate control for anomaly detection in time series

Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot

Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information

Ryusei Shingaki, manabu kuroki

Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering

Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu

Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning

Youngkyu Hong, Eunho Yang

Coordinated Proximal Policy Optimization

Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, haiyin piao

The effectiveness of feature attribution methods and its correlation with automatic evaluation scores

Giang Nguyen, Daeyoung Kim, Anh Nguyen

Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization

Pranav Subramani, Nicholas Vadivelu, Gautam Kamath

Neural Trees for Learning on Graphs

Rajat Talak, Siyi Hu, Lisa Peng, Luca Carlone

Bayesian decision-making under misspecified priors with applications to meta-learning

Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris

Towards a Unified Information-Theoretic Framework for Generalization

Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Dan Roy

A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition

Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira

Which Mutual-Information Representation Learning Objectives are Sufficient for Control?

Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems

Atara Kaplan, Dan Garber

Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality

Stefanos Leonardos, Georgios Piliouras, Kelly Spendlove

Sequence-to-Sequence Learning with Latent Neural Grammars

Yoon Kim

TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis

Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt

Certifying Robustness to Programmable Data Bias in Decision Trees

Anna Meyer, Aws Albarghouthi, Loris D'Antoni

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

Liyuan Xu, Heishiro Kanagawa, Arthur Gretton

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering

Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein

Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers

Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo, Francesco Orabona, Dan Roy

Towards Context-Agnostic Learning Using Synthetic Data

Charles Jin, Martin Rinard

Effective Meta-Regularization by Kernelized Proximal Regularization

Weisen Jiang, James T. Kwok, Yu Zhang

Learning to delegate for large-scale vehicle routing

Sirui Li, Zhongxia Yan, Cathy Wu

You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature

Kefan Dong, Jiaqi Yang, Tengyu Ma

Robust Deep Reinforcement Learning through Adversarial Loss

Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng

Lattice partition recovery with dyadic CART

OSCAR HERNAN MADRID PADILLA, Yi Yu, Alessandro Rinaldo

Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation

Hideaki Kim

Morié Attack (MA): A New Potential Risk of Screen Photos

Dantong Niu, Ruohao Guo, Yisen Wang

A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning

Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng

Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, Fei Wang

Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD

Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen

Revisiting 3D Object Detection From an Egocentric Perspective

Boyang Deng, Charles R Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou

Unintended Selection: Persistent Qualification Rate Disparities and Interventions

Reilly Raab, Yang Liu

Tensor Normal Training for Deep Learning Models

Yi Ren, Donald Goldfarb

Implicit Generative Copulas

Tim Janke, Mohamed Ghanmi, Florian Steinke

PatchGame: Learning to Signal Mid-level Patches in Referential Games

Kamal Gupta, Gowthami Somepalli, Anubhav Anubhav, Vinoj Yasanga Jayasundara Magalle Hewa, Matthias Zwicker

Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis

Thomas FEL, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux

Instance-optimal Mean Estimation Under Differential Privacy

Ziyue Huang, Yuting Liang, Ke Yi

Fast Algorithms for L_∞-constrained S-rectangular Robust MDPs

Bahram Behzadian, Marek Petrik, Chin Pang Ho

End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering

Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, Dani Yogatama

OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization

Kuniaki Saito, Donghyun Kim, Kate Saenko

Post-processing for Individual Fairness

Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin

Re-ranking for image retrieval and transductive few-shot classification

Xi SHEN, Yang Xiao, Shell Xu Hu, Othman Sbai, Mathieu Aubry

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Adrian Weller, Volkan Cevher

Identification of the Generalized Condorcet Winner in Multi-dueling Bandits

Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

ReLU Regression with Massart Noise

Ilias Diakonikolas, Jong Ho Park, Christos Tzamos

Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

Qi Chen, Changjian Shui, Mario Marchand

A Faster Decentralized Algorithm for Nonconvex Minimax Problems

Wenhan Xian, Feihu Huang, Yanfu Zhang, Heng Huang

Navigating to the Best Policy in Markov Decision Processes

Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere

SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization

Ziming Zhang, Yun Yue, Guojun Wu, Yanhua Li, Haichong Zhang

Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation

Mao Li, Kaiqi Jiang, Xinhua Zhang

Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm

Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda

Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games

Yu Bai, Chi Jin, Huan Wang, Caiming Xiong

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods

Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

Does enforcing fairness mitigate biases caused by subpopulation shift?

Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun

Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds

Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal

Reward is enough for convex MDPs

Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder P. Singh

A Max-Min Entropy Framework for Reinforcement Learning

Seungyul Han, Youngchul Sung

Fair Sortition Made Transparent

Bailey Flanigan, Gregory Kehne, Ariel D Procaccia

Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity

Vladimir Ivanov, Konstantinos Michmizos

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization

Mher Safaryan, Filip Hanzely, Peter Richtarik

Neural Production Systems

Anirudh Goyal ALIAS PARTH GOYAL, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin

A Theory of the Distortion-Perception Tradeoff in Wasserstein Space

Dror Freirich, Tomer Michaeli, Ron Meir

Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks

Hidenori Tanaka, Daniel Kunin

On the Second-order Convergence Properties of Random Search Methods

Aurelien Lucchi, Antonio Orvieto, Adamos Solomou

Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations

Yuping Luo, Tengyu Ma

Learning with Holographic Reduced Representations

Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision

Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev

Optimal Best-Arm Identification Methods for Tail-Risk Measures

Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja

Reformulating Zero-shot Action Recognition for Multi-label Actions

Alec Kerrigan, Kevin Duarte, Yogesh Rawat, Mubarak Shah

Learning Frequency Domain Approximation for Binary Neural Networks

Yixing Xu, Kai Han, Chang Xu, Yehui Tang, Chunjing XU

Fine-grained Generalization Analysis of Inductive Matrix Completion

Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft

Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel

Yixin Chen, Tonmoy Dey, Alan Kuhnle

Set Prediction in the Latent Space

Konpat Preechakul, Chawan Piansaddhayanon, Burin Naowarat, Tirasan Khandhawit, Sira Sriswasdi

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams

Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification

Clémence Réda, Andrea Tirinzoni, Rémy Degenne

Mastering Atari Games with Limited Data

Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao

Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks

Shibo Li, Robert Kirby, Shandian Zhe

Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships

Lifeng Zhang

Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration

Runzhe Wu, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang

Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers

Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer

Collapsed Variational Bounds for Bayesian Neural Networks

Marcin Tomczak, Siddharth Swaroop, Andrew Foong, Richard Turner

Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes

Sara Sangalli, Ertunc Erdil, Andeas Hötker, Olivio Donati, Ender Konukoglu

Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes

Jai Moondra, Hassan Mortagy, Swati Gupta

Machine versus Human Attention in Deep Reinforcement Learning Tasks

Suna (Sihang) Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Dana Ballard

Estimating High Order Gradients of the Data Distribution by Denoising

Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon

Dual-stream Network for Visual Recognition

Mingyuan Mao, peng gao, Renrui Zhang, Honghui Zheng, Teli Ma

Fast Training Method for Stochastic Compositional Optimization Problems

Hongchang Gao, Heng Huang

Charting and Navigating the Space of Solutions for Recurrent Neural Networks

Elia Turner, Kabir V Dabholkar, Omri Barak

Dense Unsupervised Learning for Video Segmentation

Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth

Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems

Tianyi Chen, Yuejiao Sun, Wotao Yin

Graph Neural Networks with Local Graph Parameters

Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov

Second-Order Neural ODE Optimizer

Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou