NeurIPS 2021

2,334 papers

Solving Graph-based Public Goods Games with Tree Search and Imitation Learning

Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

Revealing and Protecting Labels in Distributed Training

Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, peter chin

Reconstruction for Powerful Graph Representations

Leonardo Cotta, Christopher Morris, Bruno Ribeiro

KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network

YU HAO, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang

Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation

David Acuna, Jonah Philion, Sanja Fidler

Periodic Activation Functions Induce Stationarity

Lassi Meronen, Martin Trapp, Arno Solin

Adaptive Conformal Inference Under Distribution Shift

Isaac Gibbs, Emmanuel Candes

USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems

Guangmo Tong

Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems

Jiayang Xu, Aniruddhe Pradhan, Karthikeyan Duraisamy

Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu

Think Big, Teach Small: Do Language Models Distil Occam’s Razor?

Gonzalo Jaimovitch-Lopez, David Castellano Falcón, Cesar Ferri, José Hernández-Orallo

Beltrami Flow and Neural Diffusion on Graphs

Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation

Weitong ZHANG, Dongruo Zhou, Quanquan Gu

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup

Grounding Representation Similarity Through Statistical Testing

Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt

Smooth Bilevel Programming for Sparse Regularization

Clarice Poon, Gabriel Peyré

Differentiable Unsupervised Feature Selection based on a Gated Laplacian

Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey

Distributional Reinforcement Learning for Multi-Dimensional Reward Functions

Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin

Play to Grade: Testing Coding Games as Classifying Markov Decision Process

Allen Nie, Emma Brunskill, Chris Piech

Scalable Intervention Target Estimation in Linear Models

Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

Uniform Sampling over Episode Difficulty

Sébastien Arnold, Guneet Dhillon, Avinash Ravichandran, Stefano Soatto

Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs

Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim

Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

Đ.Khuê Lê-Huu, Karteek Alahari

Adaptive Data Augmentation on Temporal Graphs

Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang

Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization

Tian Ye, Simon S Du

Maximum Likelihood Training of Score-Based Diffusion Models

Yang Song, Conor Durkan, Iain Murray, Stefano Ermon

TransformerFusion: Monocular RGB Scene Reconstruction using Transformers

Aljaz Bozic, Pablo Palafox, Justus Thies, Angela Dai, Matthias Niessner

Systematic Generalization with Edge Transformers

Leon Bergen, Timothy O'Donnell, Dzmitry Bahdanau

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry

Regularized Softmax Deep Multi-Agent Q-Learning

Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson

The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy

Masahiro Kato, Kenichiro McAlinn, Shota Yasui

Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

On the Value of Interaction and Function Approximation in Imitation Learning

Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao

Privately Learning Subspaces

Vikrant Singhal, Thomas Steinke

Understanding Deflation Process in Over-parametrized Tensor Decomposition

Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou

Optimality and Stability in Federated Learning: A Game-theoretic Approach

Kate Donahue, Jon M. Kleinberg

Offline Reinforcement Learning as One Big Sequence Modeling Problem

Michael Janner, Qiyang Li, Sergey Levine

Gradient Starvation: A Learning Proclivity in Neural Networks

Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi

Finite Sample Analysis of Average-Reward TD Learning and Q-Learning

Sheng Zhang, Zhe Zhang, Siva Theja Maguluri

Learning Optimal Predictive Checklists

Haoran Zhang, Quaid D. Morris, Berk Ustun, Marzyeh Ghassemi

Algorithmic Instabilities of Accelerated Gradient Descent

Amit Attia, Tomer Koren

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang

Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection

Koby Bibas, Meir Feder, Tal Hassner

Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation

Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang

Iterative Connecting Probability Estimation for Networks

Yichen Qin, Linhan Yu, Yang Li

Risk-Averse Bayes-Adaptive Reinforcement Learning

Marc Rigter, Bruno Lacerda, Nick Hawes

Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters

Takanori Maehara, Hoang NT

Self-Consistent Models and Values

Greg Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel

Near Optimal Policy Optimization via REPS

Aldo Pacchiano, Jonathan N Lee, Peter L. Bartlett, Ofir Nachum

Long Short-Term Transformer for Online Action Detection

Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia

BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer

Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan

Scaling Neural Tangent Kernels via Sketching and Random Features

Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim

Subgroup Generalization and Fairness of Graph Neural Networks

Jiaqi Ma, Junwei Deng, Qiaozhu Mei

Distilling Image Classifiers in Object Detectors

Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann

Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan

CAFE: Catastrophic Data Leakage in Vertical Federated Learning

Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen

Global Filter Networks for Image Classification

Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage

Jonathan Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples

Sungyoon Lee, Woojin Lee, Jinseong Park, Jaewook Lee

Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games

Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen

Perturb-and-max-product: Sampling and learning in discrete energy-based models

Miguel Lázaro-Gredilla, Antoine Dedieu, Dileep George

Bayesian Adaptation for Covariate Shift

Aurick Zhou, Sergey Levine

Solving Soft Clustering Ensemble via k-Sparse Discrete Wasserstein Barycenter

Ruizhe Qin, Mengying Li, Hu Ding

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language

Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum

Continuous Mean-Covariance Bandits

Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang

Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images

Kwanyoung Kim, Jong Chul Ye

Alias-Free Generative Adversarial Networks

Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten

Sageflow: Robust Federated Learning against Both Stragglers and Adversaries

Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon

NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL

Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai

Sample Selection for Fair and Robust Training

Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh

Improving black-box optimization in VAE latent space using decoder uncertainty

Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal

Fuzzy Clustering with Similarity Queries

Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal

Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning

Scott Sussex, Caroline Uhler, Andreas Krause

Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael Zhang

Arbitrary Conditional Distributions with Energy

Ryan Strauss, Junier B Oliva

Towards robust vision by multi-task learning on monkey visual cortex

Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago Cadena, Kelli Restivo

Do Different Tracking Tasks Require Different Appearance Models?

Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip Torr

Mirror Langevin Monte Carlo: the Case Under Isoperimetry

Qijia Jiang

Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization

Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie P. Kaelbling

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods

Terrance Liu, Giuseppe Vietri, Steven Z. Wu

On the Importance of Gradients for Detecting Distributional Shifts in the Wild

Rui Huang, Andrew Geng, Yixuan Li

A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness

James Diffenderfer, Brian Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura

Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision

Keji He, Yan Huang, Qi Wu, Jianhua Yang, Dong An

MCMC Variational Inference via Uncorrected Hamiltonian Annealing

Tomas Geffner, Justin Domke

Subgoal Search For Complex Reasoning Tasks

Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik

The Utility of Explainable AI in Ad Hoc Human-Machine Teaming

Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew Gombolay

T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs

Changwoo Lee, Zhao Tang Luo, Huiyan Sang

Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers

Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao

(Almost) Free Incentivized Exploration from Decentralized Learning Agents

Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen

TopicNet: Semantic Graph-Guided Topic Discovery

Zhibin Duan, Yi.shi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang

Neighborhood Reconstructing Autoencoders

Yonghyeon LEE, Hyeokjun Kwon, Frank Park

Unique sparse decomposition of low rank matrices

Dian Jin, Xin Bing, Yuqian Zhang

Class-agnostic Reconstruction of Dynamic Objects from Videos

Zhongzheng Ren, Xiaoming Zhao, Alex Schwing

What training reveals about neural network complexity

Andreas Loukas, Marinos Poiitis, Stefanie Jegelka

A Contrastive Learning Approach for Training Variational Autoencoder Priors

Jyoti Aneja, Alex Schwing, Jan Kautz, Arash Vahdat

Numerical influence of ReLU’(0) on backpropagation

David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound

Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard