NeurIPS 2021

2,334 papers

On the Universality of Graph Neural Networks on Large Random Graphs

Nicolas Keriven, Alberto Bietti, Samuel Vaiter

Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases

Shashi Kant Gupta, Mengmi Zhang, CHIA-CHIEN WU, Jeremy Wolfe, Gabriel Kreiman

Antipodes of Label Differential Privacy: PATE and ALIBI

Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramer

Teachable Reinforcement Learning via Advice Distillation

Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas

Does Knowledge Distillation Really Work?

Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, Andrew G Wilson

An Empirical Study of Adder Neural Networks for Object Detection

Xinghao Chen, Chang Xu, Minjing Dong, Chunjing XU, Yunhe Wang

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions

Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou

Sim and Real: Better Together

Shirli Di-Castro, Dotan Di Castro, Shie Mannor

Nested Counterfactual Identification from Arbitrary Surrogate Experiments

Juan Correa, Sanghack Lee, Elias Bareinboim

Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret

Jean Tarbouriech, Runlong Zhou, Simon S Du, Matteo Pirotta, Michal Valko

Asynchronous Decentralized SGD with Quantized and Local Updates

Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh

Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning

Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

Loss function based second-order Jensen inequality and its application to particle variational inference

Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama

GemNet: Universal Directional Graph Neural Networks for Molecules

Johannes Gasteiger, Florian Becker, Stephan Günnemann

Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks

Tolga Birdal, Aaron Lou, Leonidas Guibas, Umut Simsekli

Functional Neural Networks for Parametric Image Restoration Problems

Fangzhou Luo, Xiaolin Wu, Yanhui Guo

Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima

Guangyuan SHI, JIAXIN CHEN, Wenlong Zhang, Li-Ming Zhan, Xiao-ming Wu

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson

Efficient Learning of Discrete-Continuous Computation Graphs

David Friede, Mathias Niepert

Sub-Linear Memory: How to Make Performers SLiM

Valerii Likhosherstov, Krzysztof M Choromanski, Jared Quincy Davis, Xingyou Song, Adrian Weller

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin

Bellman-consistent Pessimism for Offline Reinforcement Learning

Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal

Subgraph Federated Learning with Missing Neighbor Generation

Ke ZHANG, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu

Neural Tangent Kernel Maximum Mean Discrepancy

Xiuyuan Cheng, Yao Xie

Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence

Dachao Lin, Haishan Ye, Zhihua Zhang

Multi-modal Dependency Tree for Video Captioning

Wentian Zhao, Xinxiao Wu, Jiebo Luo

Speech-T: Transducer for Text to Speech and Beyond

Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen

Learning 3D Dense Correspondence via Canonical Point Autoencoder

An-Chieh Cheng, Xueting Li, Min Sun, Ming-Hsuan Yang, Sifei Liu

Visualizing the Emergence of Intermediate Visual Patterns in DNNs

Mingjie Li, Shaobo Wang, Quanshi Zhang

Neural Pseudo-Label Optimism for the Bank Loan Problem

Aldo Pacchiano, Shaun Singh, Edward Chou, Alex Berg, Jakob Foerster

A variational approximate posterior for the deep Wishart process

Sebastian Ober, Laurence Aitchison

Learnability of Linear Thresholds from Label Proportions

Rishi Saket

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno

Scalable Inference of Sparsely-changing Gaussian Markov Random Fields

Salar Fattahi, Andres Gomez

Sparse Uncertainty Representation in Deep Learning with Inducing Weights

Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li

Self-Instantiated Recurrent Units with Dynamic Soft Recursion

Aston Zhang, Yi Tay, Yikang Shen, Alvin Chan, SHUAI ZHANG

Cardinality constrained submodular maximization for random streams

Paul Liu, Aviad Rubinstein, Jan Vondrak, Junyao Zhao

Retiring Adult: New Datasets for Fair Machine Learning

Frances Ding, Moritz Hardt, John P. Miller, Ludwig Schmidt

A/B Testing for Recommender Systems in a Two-sided Marketplace

Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee

On the Representation of Solutions to Elliptic PDEs in Barron Spaces

Ziang Chen, Jianfeng Lu, Yulong Lu

Scaling Gaussian Processes with Derivative Information Using Variational Inference

Misha Padidar, Xinran Zhu, Leo Huang, Jacob Gardner, David Bindel

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions

Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco

Roto-translated Local Coordinate Frames For Interacting Dynamical Systems

Miltiadis Kofinas, Naveen Nagaraja, Efstratios Gavves

Probabilistic Forecasting: A Level-Set Approach

Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Jiayao Zhang, Hua Wang, Weijie Su

Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay

Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun

Conditioning Sparse Variational Gaussian Processes for Online Decision-making

Wesley J Maddox, Samuel Stanton, Andrew G Wilson

Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement

Aming Wu, Suqi Zhao, Cheng Deng, Wei Liu

Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization

Rohan Ghosh, Mehul Motani

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Siheng Chen

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan

Conformal Prediction using Conditional Histograms

Matteo Sesia, Yaniv Romano

Ising Model Selection Using ℓ_1-Regularized Linear Regression: A Statistical Mechanics Analysis

Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima

Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference

Dongkai Wang, Shiliang Zhang, Gang Hua

Curriculum Offline Imitating Learning

Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang

Landscape analysis of an improved power method for tensor decomposition

Joe Kileel, Timo Klock, João M Pereira

Bootstrapping the Error of Oja's Algorithm

Robert Lunde, Purnamrita Sarkar, Rachel Ward

A 3D Generative Model for Structure-Based Drug Design

Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng

Conformal Time-series Forecasting

Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar

Adversarial Robustness with Semi-Infinite Constrained Learning

Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro

A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning

Pan Zhou, Caiming Xiong, Xiaotong Yuan, Steven Chu Hong Hoi

A first-order primal-dual method with adaptivity to local smoothness

Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li

Refined Learning Bounds for Kernel and Approximate k-Means

Yong Liu

Provably efficient, succinct, and precise explanations

Guy Blanc, Jane Lange, Li-Yang Tan

On Success and Simplicity: A Second Look at Transferable Targeted Attacks

Zhengyu Zhao, Zhuoran Liu, Martha Larson

Learning to Combine Per-Example Solutions for Neural Program Synthesis

Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis

Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, Sanja Fidler

The Semi-Random Satisfaction of Voting Axioms

Lirong Xia

Bubblewrap: Online tiling and real-time flow prediction on neural manifolds

Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John C. Pearson

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

Prashant Khanduri, PRANAY SHARMA, Haibo Yang, Mingyi Hong, Jia Liu

Multi-Person 3D Motion Prediction with Multi-Range Transformers

Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang

Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets

Max Ryabinin, Andrey Malinin, Mark Gales

Searching for Efficient Transformers for Language Modeling

David So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling

Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization

Jialun Zhang, Salar Fattahi, Richard Y Zhang

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang

Hyperbolic Procrustes Analysis Using Riemannian Geometry

Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon

Data Sharing and Compression for Cooperative Networked Control

Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang

Group Equivariant Subsampling

Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee W. Teh

Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

Jiaming Liu, Salman Asif, Brendt Wohlberg, Ulugbek Kamilov

ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE

Qingzhong Ai, LIRONG HE, SHIYU LIU, Zenglin Xu

COHESIV: Contrastive Object and Hand Embedding Segmentation In Video

Dandan Shan, Richard Higgins, David Fouhey

Computer-Aided Design as Language

Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti

The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains

Daron Anderson, Douglas Leith

Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets

Irene Solaiman, Christy Dennison

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data

Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

History Aware Multimodal Transformer for Vision-and-Language Navigation

Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, Ivan Laptev

iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder

Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li

Fast Pure Exploration via Frank-Wolfe

Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere

On UMAP's True Loss Function

Sebastian Damrich, Fred A. Hamprecht

Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning

Kento Nozawa, Issei Sato

Dynamic Grained Encoder for Vision Transformers

Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor

Shape Registration in the Time of Transformers

Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi

A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval

Xiu-Shen Wei, Yang Shen, Xuhao Sun, Han-Jia Ye, Jian Yang

On the Variance of the Fisher Information for Deep Learning

Alexander Soen, Ke Sun

Machine learning structure preserving brackets for forecasting irreversible processes

Kookjin Lee, Nathaniel Trask, Panos Stinis

Contrastive Laplacian Eigenmaps

Hao Zhu, Ke Sun, Peter Koniusz