NeurIPS 2021

2,334 papers

Combinatorial Pure Exploration with Bottleneck Reward Function

Yihan Du, Yuko Kuroki, Wei Chen

POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples

Duong Le, Khoi Duc Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen

Well-tuned Simple Nets Excel on Tabular Datasets

Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka

Analytic Insights into Structure and Rank of Neural Network Hessian Maps

Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann

LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park

Partial success in closing the gap between human and machine vision

Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration

Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, Slobodan Ilic

Particle Cloud Generation with Message Passing Generative Adversarial Networks

Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei

Efficient Combination of Rematerialization and Offloading for Training DNNs

Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova

Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction

Dominik Stöger, Mahdi Soltanolkotabi

Efficient Training of Visual Transformers with Small Datasets

Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri

MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar

Bias and variance of the Bayesian-mean decoder

Arthur Prat-Carrabin, Michael Woodford

Learning Riemannian metric for disease progression modeling

Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley DURRLEMAN

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang

Estimating Multi-cause Treatment Effects via Single-cause Perturbation

Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar

Optimal Sketching for Trace Estimation

Shuli Jiang, Hai Pham, David Woodruff, Richard Zhang

Adaptive Denoising via GainTuning

Sreyas Mohan, Joshua L Vincent, Ramon Manzorro, Peter Crozier, Carlos Fernandez-Granda

Off-Policy Risk Assessment in Contextual Bandits

Audrey Huang, Liu Leqi, Zachary Lipton, Kamyar Azizzadenesheli

Tracking People with 3D Representations

Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik

Mixability made efficient: Fast online multiclass logistic regression

Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi

Learning State Representations from Random Deep Action-conditional Predictions

Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Satinder P. Singh, Richard L. Lewis

Adaptive Risk Minimization: Learning to Adapt to Domain Shift

Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine

Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes

Mohamad Amin Sharifi Kolarijani, Gyula Max, Peyman Mohajerin Esfahani

MERLOT: Multimodal Neural Script Knowledge Models

Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park

Spatio-Temporal Variational Gaussian Processes

Oliver Hamelijnck, William Wilkinson, Niki Loppi, Arno Solin, Theodoros Damoulas

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu

Probabilistic Transformer For Time Series Analysis

Binh Tang, David S Matteson

Fast rates for prediction with limited expert advice

El Mehdi Saad, Gilles Blanchard

Robust and Decomposable Average Precision for Image Retrieval

Elias Ramzi, Nicolas Thome, Clément Rambour, Nicolas Audebert, Xavier Bitot

Sampling with Trusthworthy Constraints: A Variational Gradient Framework

Xingchao Liu, Xin Tong, Qiang Liu

Multi-Label Learning with Pairwise Relevance Ordering

Ming-Kun Xie, Sheng-jun Huang

Slice Sampling Reparameterization Gradients

David Zoltowski, Diana Cai, Ryan P. Adams

Towards a Theoretical Framework of Out-of-Distribution Generalization

Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li

Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

Minguk Kang, Woohyeon Shim, Minsu Cho, Jaesik Park

Learning Semantic Representations to Verify Hardware Designs

Shobha Vasudevan, Wenjie (Joe) Jiang, David Bieber, Rishabh Singh, hamid shojaei

On the Equivalence between Neural Network and Support Vector Machine

Yilan Chen, Wei Huang, Lam Nguyen, Tsui-Wei Weng

Interpolation can hurt robust generalization even when there is no noise

Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang

Black Box Probabilistic Numerics

Onur Teymur, Christopher Foley, Philip Breen, Toni Karvonen, Chris J Oates

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan Reddy

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks

Yuhang Li, Yufei Guo, Shanghang Zhang, Shikuang Deng, Yongqing Hai

Active Learning of Convex Halfspaces on Graphs

Maximilian Thiessen, Thomas Gaertner

The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization

Daniel LeJeune, Hamid Javadi, Richard Baraniuk

Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das

How Modular should Neural Module Networks Be for Systematic Generalization?

Vanessa D'Amario, Tomotake Sasaki, Xavier Boix

Editing a classifier by rewriting its prediction rules

Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba

Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models

Yi Sui, Ga Wu, Scott Sanner

Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits

Orestis Papadigenopoulos, Constantine Caramanis

Adaptive Diffusion in Graph Neural Networks

Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang

PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization

Benyuan Sun, Hongxing Huo, Yi Yang, Bo Bai

Intriguing Properties of Vision Transformers

Muhammad Muzammal Naseer, Kanchana Ranasinghe, Salman H Khan, Munawar Hayat, Fahad Shahbaz Khan

The Difficulty of Passive Learning in Deep Reinforcement Learning

Georg Ostrovski, Pablo Samuel Castro, Will Dabney

Unbalanced Optimal Transport through Non-negative Penalized Linear Regression

Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, Gilles Gasso

Probabilistic Margins for Instance Reweighting in Adversarial Training

qizhou wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem

Adarsh Barik, Jean Honorio

Meta-learning to Improve Pre-training

Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David K. Duvenaud

Change Point Detection via Multivariate Singular Spectrum Analysis

Arwa Alanqary, Abdullah Alomar, Devavrat Shah

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation

Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, alexander gimson

Bandits with Knapsacks beyond the Worst Case

Karthik Abinav Sankararaman, Aleksandrs Slivkins

Identity testing for Mallows model

Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Emmanouil Zampetakis

Learning to Compose Visual Relations

Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, Antonio Torralba

A mechanistic multi-area recurrent network model of decision-making

Michael Kleinman, Chandramouli Chandrasekaran, Jonathan Kao

Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations

Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi

XDO: A Double Oracle Algorithm for Extensive-Form Games

Stephen Mcaleer, JB Lanier, Kevin A Wang, Pierre Baldi, Roy Fox

Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization

Qi Deng, Wenzhi Gao

COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

Yu Meng, Chenyan Xiong, Payal Bajaj, saurabh tiwary, Paul Bennett

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair

Zimin Chen, Vincent J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-antoine Manzagol

Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication

Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej, Piotr Miłoś

Optimal Policies Tend To Seek Power

Alex Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He

A Note on Sparse Generalized Eigenvalue Problem

Yunfeng Cai, Guanhua Fang, Ping Li

NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs

Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang

Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model

Bingyan Wang, Yuling Yan, Jianqing Fan

Stochastic Anderson Mixing for Nonconvex Stochastic Optimization

Fuchao Wei, Chenglong Bao, Yang Liu

Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation

Bingchen Zhao, Kai Han

Cycle Self-Training for Domain Adaptation

Hong Liu, Jianmin Wang, Mingsheng Long

Adversarially Robust Change Point Detection

Mengchu Li, Yi Yu

Multiclass versus Binary Differentially Private PAC Learning

Satchit Sivakumar, Mark Bun, Marco Gaboardi

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses

Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee

LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning

Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon

Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Maximilian Seitzer, Bernhard Schölkopf, Georg Martius

Does Preprocessing Help Training Over-parameterized Neural Networks?

Zhao Song, Shuo Yang, Ruizhe Zhang

Online Active Learning with Surrogate Loss Functions

Giulia DeSalvo, Claudio Gentile, Tobias Sommer Thune

A Variational Perspective on Diffusion-Based Generative Models and Score Matching

Chin-Wei Huang, Jae Hyun Lim, Aaron C. Courville

Coresets for Time Series Clustering

Lingxiao Huang, K Sudhir, Nisheeth Vishnoi

DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang

Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases

Jeffrey Adams, Niels Hansen, Kun Zhang

Learning in Non-Cooperative Configurable Markov Decision Processes

Giorgia Ramponi, Alberto Maria Metelli, Alessandro Concetti, Marcello Restelli

Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding

Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng

Counterexample Guided RL Policy Refinement Using Bayesian Optimization

Briti Gangopadhyay, Pallab Dasgupta

SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

Muhammad Akhtar Munir, Muhammad Haris Khan, M. Sarfraz, Mohsen Ali

Average-Reward Learning and Planning with Options

Yi Wan, Abhishek Naik, Rich Sutton

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds

Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar

Encoding Spatial Distribution of Convolutional Features for Texture Representation

Yong Xu, Feng Li, Zhile Chen, Jinxiu Liang, Yuhui Quan

Design of Experiments for Stochastic Contextual Linear Bandits

Andrea Zanette, Kefan Dong, Jonathan N Lee, Emma Brunskill

Local Disentanglement in Variational Auto-Encoders Using Jacobian L_1 Regularization

Travers Rhodes, Daniel D. Lee

Double/Debiased Machine Learning for Dynamic Treatment Effects

Greg Lewis, Vasilis Syrgkanis

Aligning Pretraining for Detection via Object-Level Contrastive Learning

Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin

SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi

On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond

Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos