NeurIPS 2021

2,334 papers

Controllable and Compositional Generation with Latent-Space Energy-Based Models

Weili Nie, Arash Vahdat, Anima Anandkumar

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations

Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan

For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets

Brian Trippe, Hilary Finucane, Tamara Broderick

Settling the Variance of Multi-Agent Policy Gradients

Jakub Grudzien Kuba, Muning Wen, Linghui Meng, shangding gu, Haifeng Zhang

Alignment Attention by Matching Key and Query Distributions

Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, Mingyuan Zhou

HNPE: Leveraging Global Parameters for Neural Posterior Estimation

Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort

Detecting Anomalous Event Sequences with Temporal Point Processes

Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms

Chi Jin, Qinghua Liu, Sobhan Miryoosefi

A universal probabilistic spike count model reveals ongoing modulation of neural variability

David Liu, Máté Lengyel

Structured Reordering for Modeling Latent Alignments in Sequence Transduction

bailin wang, Mirella Lapata, Ivan Titov

Progressive Coordinate Transforms for Monocular 3D Object Detection

Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He

Video Instance Segmentation using Inter-Frame Communication Transformers

Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim

Better Algorithms for Individually Fair k-Clustering

Maryam Negahbani, Deeparnab Chakrabarty

Curriculum Learning for Vision-and-Language Navigation

Jiwen Zhang, zhongyu wei, Jianqing Fan, Jiajie Peng

Channel Permutations for N:M Sparsity

Jeff Pool, Chong Yu

Implicit Transformer Network for Screen Content Image Continuous Super-Resolution

Jingyu Yang, Sheng Shen, Huanjing Yue, Kun Li

Learning Student-Friendly Teacher Networks for Knowledge Distillation

Dae Young Park, Moon-Hyun Cha, changwook jeong, Daesin Kim, Bohyung Han

Beyond Bandit Feedback in Online Multiclass Classification

Dirk van der Hoeven, Federico Fusco, Nicolò Cesa-bianchi

Representing Long-Range Context for Graph Neural Networks with Global Attention

Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez

Towards Stable and Robust AdderNets

Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu

Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser

Zahra Kadkhodaie, Eero P. Simoncelli

Learning to Learn Dense Gaussian Processes for Few-Shot Learning

Ze Wang, Zichen Miao, Xiantong Zhen, Qiang Qiu

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

Alek Dimitriev, Mingyuan Zhou

Demystifying and Generalizing BinaryConnect

Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia

A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference

Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck

Exploiting Opponents Under Utility Constraints in Sequential Games

Martino Bernasconi-de-Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi

MST: Masked Self-Supervised Transformer for Visual Representation

Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu

Direct Multi-view Multi-person 3D Pose Estimation

Tao Wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng

The Emergence of Objectness: Learning Zero-shot Segmentation from Videos

Runtao Liu, Zhirong Wu, Stella X. Yu, Stephen Lin

Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms

Jeremy Seeman, Matthew Reimherr, Aleksandra Slavković

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning

Jingfeng Wu, Vladimir braverman, Lin Yang

Sifting through the noise: Universal first-order methods for stochastic variational inequalities

Kimon Antonakopoulos, Thomas Pethick, Ali Kavis, Panayotis Mertikopoulos, Volkan Cevher

SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency

Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslan Salakhutdinov

Rectifying the Shortcut Learning of Background for Few-Shot Learning

Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang

Outcome-Driven Reinforcement Learning via Variational Inference

Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine

Shape As Points: A Differentiable Poisson Solver

Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys

Self-Supervised GANs with Label Augmentation

Liang Hou, Huawei Shen, Qi Cao, Xueqi Cheng

Stochastic $L^\natural$-convex Function Minimization

Haixiang Zhang, Zeyu Zheng, Javad Lavaei

Glance-and-Gaze Vision Transformer

Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan L. Yuille

Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias

Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora

Interactive Label Cleaning with Example-based Explanations

Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini

Consistency Regularization for Variational Auto-Encoders

Samarth Sinha, Adji Bousso Dieng

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals

Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi

Pareto Domain Adaptation

fangrui lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu

MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data

Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen

Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings

Ming Yin, Yu-Xiang Wang

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris

Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning

Siyuan Zhang, Nan Jiang

Tactical Optimism and Pessimism for Deep Reinforcement Learning

Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan

Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach

Anindya Sarkar, Anirban Sarkar, Sowrya Gali, Vineeth N Balasubramanian

Understanding Interlocking Dynamics of Cooperative Rationalization

Mo Yu, Yang Zhang, Shiyu Chang, Tommi Jaakkola

Non-asymptotic convergence bounds for Wasserstein approximation using point clouds

Quentin Mérigot, Filippo Santambrogio, Clément SARRAZIN

Learning in Multi-Stage Decentralized Matching Markets

Xiaowu Dai, Michael I. Jordan

TokenLearner: Adaptive Space-Time Tokenization for Videos

Michael Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova

Towards optimally abstaining from prediction with OOD test examples

Adam Kalai, Varun Kanade

Collaborative Causal Discovery with Atomic Interventions

Raghavendra Addanki, Shiva P. Kasiviswanathan

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen

Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect

Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann

Proportional Participatory Budgeting with Additive Utilities

Dominik Peters, Grzegorz Pierczyński, Piotr Skowron

On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)

Zhiyuan Li, Sadhika Malladi, Sanjeev Arora

Universal Approximation Using Well-Conditioned Normalizing Flows

Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski

Pretraining Representations for Data-Efficient Reinforcement Learning

Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin

An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks

Shashank Rajput, Kartik Sreenivasan, Dimitris Papailiopoulos, Amin Karbasi

The Inductive Bias of Quantum Kernels

Jonas Kübler, Simon Buchholz, Bernhard Schölkopf

OctField: Hierarchical Implicit Functions for 3D Modeling

Jia-Heng Tang, Weikai Chen, jie Yang, Bo Wang, Songrun Liu

A Multi-Implicit Neural Representation for Fonts

Pradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher, Hailin Jin

Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote

Yi-Shan Wu, Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen

Out-of-Distribution Generalization in Kernel Regression

Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan

SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning

Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas

Learning with Labeling Induced Abstentions

Kareem Amin, Giulia DeSalvo, Afshin Rostamizadeh

Constrained Two-step Look-Ahead Bayesian Optimization

Yunxiang Zhang, Xiangyu Zhang, Peter Frazier

Continual Auxiliary Task Learning

Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation

Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon

Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning

Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, Yang Yu

Variational Bayesian Optimistic Sampling

Brendan O'Donoghue, Tor Lattimore

Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers

Mandela Patrick, Dylan Campbell, Yuki Asano, Ishan Misra, Florian Metze

Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence

Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis

Learning with User-Level Privacy

Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

Global Convergence of Online Optimization for Nonlinear Model Predictive Control

Sen Na

Causal Navigation by Continuous-time Neural Networks

Charles Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

Kimia Nadjahi, Alain Durmus, Pierre E Jacob, Roland Badeau, Umut Simsekli

Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning

Guanlin Liu, Lifeng LAI

On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning

Julian Zilly, Alessandro Achille, Andrea Censi, Emilio Frazzoli

A Probabilistic State Space Model for Joint Inference from Differential Equations and Data

Jonathan Schmidt, Nicholas Krämer, Philipp Hennig

Identifiability in inverse reinforcement learning

Haoyang Cao, Samuel Cohen, Lukasz Szpruch

Answering Complex Causal Queries With the Maximum Causal Set Effect

Zachary Markovich

Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

Songyuan Zhang, ZHANGJIE CAO, Dorsa Sadigh, Yanan Sui

Non-asymptotic Error Bounds for Bidirectional GANs

Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang

Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones

Yushi Bai, Zhitao Ying, Hongyu Ren, Jure Leskovec

How can classical multidimensional scaling go wrong?

Rishi Sonthalia, Greg Van Buskirk, Benjamin Raichel, Anna Gilbert

Differential Privacy Over Riemannian Manifolds

Matthew Reimherr, Karthik Bharath, Carlos Soto

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose

Shih-Yang Su, Frank Yu, Michael Zollhoefer, Helge Rhodin

Evaluating Efficient Performance Estimators of Neural Architectures

Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang

Object-aware Contrastive Learning for Debiased Scene Representation

Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin

Learning to Assimilate in Chaotic Dynamical Systems

Michael McCabe, Jed Brown

EDGE: Explaining Deep Reinforcement Learning Policies

Wenbo Guo, Xian Wu, Usmann Khan, Xinyu Xing

FACMAC: Factored Multi-Agent Centralised Policy Gradients

Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr