NeurIPS 2021

2,334 papers

Uncertainty Quantification and Deep Ensembles

Rahul Rahaman, alexandre thiery

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio

Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information

Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim

Row-clustering of a Point Process-valued Matrix

Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan

XCiT: Cross-Covariance Image Transformers

Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

Xingang Pan, Xudong XU, Chen Change Loy, Christian Theobalt, Bo Dai

Graphical Models in Heavy-Tailed Markets

Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang

Policy Learning Using Weak Supervision

Jingkang Wang, Hongyi Guo, Zhaowei Zhu, Yang Liu

Optimality of variational inference for stochasticblock model with missing links

Solenne Gaucher, Olga Klopp

Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport

Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao

Matching a Desired Causal State via Shift Interventions

Jiaqi Zhang, Chandler Squires, Caroline Uhler

Reverse engineering learned optimizers reveals known and novel mechanisms

Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-dickstein

Adder Attention for Vision Transformer

Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang

Convergence and Alignment of Gradient Descent with Random Backpropagation Weights

Ganlin Song, Ruitu Xu, John D. Lafferty

On learning sparse vectors from mixture of responses

Nikita Polyanskii

Algorithmic stability and generalization of an unsupervised feature selection algorithm

xinxing wu, Qiang Cheng

Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data

HaiYing Wang, Aonan Zhang, Chong Wang

Time-independent Generalization Bounds for SGLD in Non-convex Settings

Tyler Farghly, Patrick Rebeschini

CogView: Mastering Text-to-Image Generation via Transformers

Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou

Bounds all around: training energy-based models with bidirectional bounds

Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg

When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B. Aditya Prakash

Predicting Molecular Conformation via Dynamic Graph Score Matching

Shitong Luo, Chence Shi, Minkai Xu, Jian Tang

Provably efficient multi-task reinforcement learning with model transfer

Chicheng Zhang, Zhi Wang

Knowledge-Adaptation Priors

Mohammad Emtiyaz Khan, Siddharth Swaroop

Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates

Alp Yurtsever, Alex Gu, Suvrit Sra

Model Selection for Bayesian Autoencoders

Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla

Boost Neural Networks by Checkpoints

Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, xian wei

Pipeline Combinators for Gradual AutoML

Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avi Shinnar

Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations

Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen

Fast and Memory Efficient Differentially Private-SGD via JL Projections

Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen

Decoupling the Depth and Scope of Graph Neural Networks

Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich

Referring Transformer: A One-step Approach to Multi-task Visual Grounding

Muchen Li, Leonid Sigal

Only Train Once: A One-Shot Neural Network Training And Pruning Framework

Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang

Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning

Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis

Atsushi Nitanda, Denny Wu, Taiji Suzuki

Space-time Mixing Attention for Video Transformer

Adrian Bulat, Juan Manuel Perez Rua, Swathikiran Sudhakaran, Brais Martinez, Georgios Tzimiropoulos

Directed Graph Contrastive Learning

Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li

Grounding inductive biases in natural images: invariance stems from variations in data

Diane Bouchacourt, Mark Ibrahim, Ari Morcos

Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization

Arnab Maiti, Vishakha Patil, Arindam Khan

Compressive Visual Representations

Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer

OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression

Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li

Fast Axiomatic Attribution for Neural Networks

Robin Hesse, Simone Schaub-Meyer, Stefan Roth

How to transfer algorithmic reasoning knowledge to learn new algorithms?

Louis-Pascal Xhonneux, Andreea-Ioana Deac, Petar Veličković, Jian Tang

Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

Sameera Ramasinghe, Moshiur Farazi, Salman H Khan, Nick Barnes, Stephen Gould

Tracking Without Re-recognition in Humans and Machines

Drew Linsley, Girik Malik, Junkyung Kim, Lakshmi Narasimhan Govindarajan, Ennio Mingolla

Stochastic bandits with groups of similar arms.

Fabien Pesquerel, Hassan SABER, Odalric-ambrym Maillard

Adversarial Teacher-Student Representation Learning for Domain Generalization

Fu-En Yang, Yuan-Chia Cheng, Zu-Yun Shiau, Yu-Chiang Frank Wang

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

Qitian Wu, Chenxiao Yang, Junchi Yan

Exploiting a Zoo of Checkpoints for Unseen Tasks

Jiaji Huang, Qiang Qiu, Kenneth W. Church

PSD Representations for Effective Probability Models

Alessandro Rudi, Carlo Ciliberto

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise

Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B Grosse

REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision

Mihai Fieraru, Mihai Zanfir, Teodor Szente, Eduard Bazavan, Vlad Olaru

Scheduling jobs with stochastic holding costs

Dabeen Lee, Milan Vojnovic

Optimal Underdamped Langevin MCMC Method

Zhengmian Hu, Feihu Huang, Heng Huang

Fine-Grained Zero-Shot Learning with DNA as Side Information

Sarkhan Badirli, Zeynep Akata, George Mohler, Christine Picard, Mehmet M Dundar

Understanding the Effect of Stochasticity in Policy Optimization

Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole

Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering

Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, Fredo Durand

Scalable Diverse Model Selection for Accessible Transfer Learning

Daniel Bolya, Rohit Mittapalli, Judy Hoffman

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang

Bayesian Optimization with High-Dimensional Outputs

Wesley J Maddox, Maximilian Balandat, Andrew G Wilson, Eytan Bakshy

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning

Aurelien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark van der Laan

Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics

Carles Domingo i Enrich, Youssef Mroueh

Conservative Offline Distributional Reinforcement Learning

Yecheng Ma, Dinesh Jayaraman, Osbert Bastani

Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation

Weiming Liu, Jiajie Su, Chaochao Chen, Xiaolin Zheng

Agent Modelling under Partial Observability for Deep Reinforcement Learning

Georgios Papoudakis, Filippos Christianos, Stefano Albrecht

Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time

Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing

Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F Balcan

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu

Container: Context Aggregation Networks

peng gao, Jiasen Lu, hongsheng Li, Roozbeh Mottaghi, Aniruddha Kembhavi

Adversarial Robustness with Non-uniform Perturbations

Ecenaz Erdemir, Jeffrey Bickford, Luca Melis, Sergul Aydore

Bridging the Imitation Gap by Adaptive Insubordination

Luca Weihs, Unnat Jain, Iou-Jen Liu, Jordi Salvador, Svetlana Lazebnik

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization

Deeksha Adil, Brian Bullins, Sushant Sachdeva

Tighter Expected Generalization Error Bounds via Wasserstein Distance

Borja Rodríguez Gálvez, German Bassi, Ragnar Thobaben, Mikael Skoglund

Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity

Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien

Support Recovery of Sparse Signals from a Mixture of Linear Measurements

Soumyabrata Pal, Arya Mazumdar, Venkata Gandikota

Adapting to function difficulty and growth conditions in private optimization

Hilal Asi, Daniel Levy, John C. Duchi

Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements

Kimon Antonakopoulos, Panayotis Mertikopoulos

Functional Regularization for Reinforcement Learning via Learned Fourier Features

Alexander Li, Deepak Pathak

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations

Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan

Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare

Joshua Kavner, Lirong Xia

Metropolis-Hastings Data Augmentation for Graph Neural Networks

Hyeonjin Park, Seunghun Lee, Sihyeon Kim, Jinyoung Park, Jisu Jeong

Unfolding Taylor's Approximations for Image Restoration

man zhou, Xueyang Fu, Zeyu Xiao, Gang Yang, Aiping Liu

Neural Program Generation Modulo Static Analysis

Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas Reps, Swarat Chaudhuri

Label-Imbalanced and Group-Sensitive Classification under Overparameterization

Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, CHRISTOS THRAMPOULIDIS

SOPE: Spectrum of Off-Policy Estimators

Christina Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum

Backdoor Attack with Imperceptible Input and Latent Modification

Khoa Doan, Yingjie Lao, Ping Li

Revisiting Deep Learning Models for Tabular Data

Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko

Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition

Jayadev Acharya, Clement Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination

Dylan J Foster, Akshay Krishnamurthy

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams

Conflict-Averse Gradient Descent for Multi-task learning

Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance

Hongjian Wang, Mert Gurbuzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A Erdogdu

SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning

Talip Ucar, Ehsan Hajiramezanali, Lindsay Edwards

Federated Graph Classification over Non-IID Graphs

Han Xie, Jing Ma, Li Xiong, Carl Yang

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu

Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions

Yin Tat Lee, Ruoqi Shen, Kevin Tian

Bandit Phase Retrieval

Tor Lattimore, Botao Hao

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence

Agustinus Kristiadi, Matthias Hein, Philipp Hennig