NeurIPS 2021

2,334 papers

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms

Alexander Camuto, George Deligiannidis, Murat A Erdogdu, Mert Gurbuzbalaban, Umut Simsekli

EIGNN: Efficient Infinite-Depth Graph Neural Networks

Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao

Efficient and Accurate Gradients for Neural SDEs

Patrick Kidger, James Foster, Xuechen (Chen) Li, Terry Lyons

Learning Models for Actionable Recourse

Alexis Ross, Himabindu Lakkaraju, Osbert Bastani

Taxonomizing local versus global structure in neural network loss landscapes

Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E Gonzalez

Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning

Danruo DENG, Guangyong Chen, Jianye Hao, Qiong Wang, Pheng-Ann Heng

Monte Carlo Tree Search With Iteratively Refining State Abstractions

Samuel Sokota, Caleb Y Ho, Zaheen Ahmad, J. Zico Kolter

Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, Rishabh Iyer

On the Sample Complexity of Learning under Geometric Stability

Alberto Bietti, Luca Venturi, Joan Bruna

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families

Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam

Heavy Ball Neural Ordinary Differential Equations

Hedi Xia, Vai Suliafu, Hangjie Ji, Tan Nguyen, Andrea Bertozzi

Online Variational Filtering and Parameter Learning

Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet

Knowledge-inspired 3D Scene Graph Prediction in Point Cloud

Shoulong Zhang, Shuai Li, Aimin Hao, Hong Qin

Partition and Code: learning how to compress graphs

Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael Bronstein

All Tokens Matter: Token Labeling for Training Better Vision Transformers

Zi-Hang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi

PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen

Fitting summary statistics of neural data with a differentiable spiking network simulator

Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner

Neural Distance Embeddings for Biological Sequences

Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Veličković, Jure Leskovec

BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining

Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh

Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning

Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu

Decentralized Learning in Online Queuing Systems

Flore Sentenac, Etienne Boursier, Vianney Perchet

Statistically and Computationally Efficient Linear Meta-representation Learning

Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Autonomous Reinforcement Learning via Subgoal Curricula

Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Behavior From the Void: Unsupervised Active Pre-Training

Hao Liu, Pieter Abbeel

Towards Multi-Grained Explainability for Graph Neural Networks

Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

The Image Local Autoregressive Transformer

Chenjie Cao, Yuxin Hong, Xiang Li, Chengrong Wang, Chengming Xu

Relative Flatness and Generalization

Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, Mario Boley

FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang

On Locality of Local Explanation Models

Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C Holmes

Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence

Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang

Improved Transformer for High-Resolution GANs

Long Zhao, Zizhao Zhang, Ting Chen, Dimitris Metaxas, Han Zhang

Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices

Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard

Fast Certified Robust Training with Short Warmup

Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-jui Hsieh

Decentralized Q-learning in Zero-sum Markov Games

Muhammed Sayin, Kaiqing Zhang, David Leslie, Tamer Basar, Asuman Ozdaglar

Understanding the Under-Coverage Bias in Uncertainty Estimation

Yu Bai, Song Mei, Huan Wang, Caiming Xiong

Training Neural Networks is ER-complete

Mikkel Abrahamsen, Linda Kleist, Tillmann Miltzow

A Unified Approach to Fair Online Learning via Blackwell Approachability

Evgenii Chzhen, Christophe Giraud, Gilles Stoltz

Conformal Bayesian Computation

Edwin Fong, Chris C Holmes

Supervising the Transfer of Reasoning Patterns in VQA

Corentin Kervadec, Christian Wolf, Grigory Antipov, Moez Baccouche, Madiha Nadri

SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning

Aaron Chan, Jiashu Xu, Boyuan Long, Soumya Sanyal, Tanishq Gupta

Self-Supervised Learning Disentangled Group Representation as Feature

Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang

IRM—when it works and when it doesn't: A test case of natural language inference

Yana Dranker, He He, Yonatan Belinkov

Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices

Max Ryabinin, Eduard Gorbunov, Vsevolod Plokhotnyuk, Gennady Pekhimenko

Reinforcement learning for optimization of variational quantum circuit architectures

Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko

Training Over-parameterized Models with Non-decomposable Objectives

Harikrishna Narasimhan, Aditya K Menon

It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems

Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin

Learning curves of generic features maps for realistic datasets with a teacher-student model

Bruno Loureiro, Cedric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala

On the Frequency Bias of Generative Models

Katja Schwarz, Yiyi Liao, Andreas Geiger

Deep Contextual Video Compression

Jiahao Li, Bin Li, Yan Lu

ErrorCompensatedX: error compensation for variance reduced algorithms

Hanlin Tang, Yao Li, Ji Liu, Ming Yan

Learning latent causal graphs via mixture oracles

Bohdan Kivva, Goutham Rajendran, Bryon Aragam, Pradeep K Ravikumar, Pradeep K. Ravikumar

Remember What You Want to Forget: Algorithms for Machine Unlearning

Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

No-Press Diplomacy from Scratch

Anton Bakhtin, David Wu, Adam Lerer, Noam Brown

Locality Sensitive Teaching

Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann

Manipulating SGD with Data Ordering Attacks

I Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot

Distributed Machine Learning with Sparse Heterogeneous Data

Dominic Richards, Sahand Negahban, Patrick Rebeschini

Emergent Communication of Generalizations

Jesse Mu, Noah Goodman

Structured Denoising Diffusion Models in Discrete State-Spaces

Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

Teaching an Active Learner with Contrastive Examples

Chaoqi Wang, Adish Singla, Yuxin Chen

The Pareto Frontier of model selection for general Contextual Bandits

Teodor Vanislavov Marinov, Julian Zimmert

MOMA: Multi-Object Multi-Actor Activity Parsing

Zelun Luo, Wanze Xie, Siddharth Kapoor, Yiyun Liang, Michael Cooper

A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning

Gugan Chandrashekhar Thoppe, Bhumesh Kumar

Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL

Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang

Handling Long-tailed Feature Distribution in AdderNets

Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu

BooVAE: Boosting Approach for Continual Learning of VAE

Evgenii Egorov, Anna Kuzina, Evgeny Burnaev

Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data

Amirreza Farnoosh, Sarah Ostadabbas

Per-Pixel Classification is Not All You Need for Semantic Segmentation

Bowen Cheng, Alex Schwing, Alexander Kirillov

Learning Fast-Inference Bayesian Networks

Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons

Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn

Image Generation using Continuous Filter Atoms

Ze Wang, Seunghyun Hwang, Zichen Miao, Qiang Qiu

Deconditional Downscaling with Gaussian Processes

Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

Julian Katz-Samuels, Blake Mason, Kevin G. Jamieson, Rob Nowak

Weighted model estimation for offline model-based reinforcement learning

Toru Hishinuma, Kei Senda

Robust Auction Design in the Auto-bidding World

Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning

Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi

The Implicit Bias of Minima Stability: A View from Function Space

Rotem Mulayoff, Tomer Michaeli, Daniel Soudry

Post-Training Sparsity-Aware Quantization

Gil Shomron, Freddy Gabbay, Samer Kurzum, Uri Weiser

Long-Short Transformer: Efficient Transformers for Language and Vision

Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling

Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet

Structure-Aware Random Fourier Kernel for Graphs

Jinyuan Fang, Qiang Zhang, Zaiqiao Meng, Shangsong Liang

Moser Flow: Divergence-based Generative Modeling on Manifolds

Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman

Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors

Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett

TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness

Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen

An Information-theoretic Approach to Distribution Shifts

Marco Federici, Ryota Tomioka, Patrick Forré

Relative Uncertainty Learning for Facial Expression Recognition

Yuhang Zhang, Chengrui Wang, Weihong Deng

Regret Minimization Experience Replay in Off-Policy Reinforcement Learning

Xu-Hui Liu, Zhenghai Xue, Jingcheng Pang, Shengyi Jiang, Feng Xu

Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks

Rong Zhu, Mattia Rigotti

Emergent Communication under Varying Sizes and Connectivities

Jooyeon Kim, Alice Oh

Sliced Mutual Information: A Scalable Measure of Statistical Dependence

Ziv Goldfeld, Kristjan Greenewald

Hash Layers For Large Sparse Models

Stephen Roller, Sainbayar Sukhbaatar, arthur szlam, Jason Weston

Large-Scale Learning with Fourier Features and Tensor Decompositions

Frederiek Wesel, Kim Batselier

PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning

Neehar Peri, Michael Curry, Samuel Dooley, John Dickerson

Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

Christian Gumbsch, Martin V. Butz, Georg Martius

De-randomizing MCMC dynamics with the diffusion Stein operator

Zheyang Shen, Markus Heinonen, Samuel Kaski

Generating High-Quality Explanations for Navigation in Partially-Revealed Environments

Gregory Stein

Projected GANs Converge Faster

Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger

Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial

Yang Liu, Jialu Wang

Differentially Private Learning with Adaptive Clipping

Galen Andrew, Om Thakkar, Brendan Mcmahan, Swaroop Ramaswamy