NeurIPS 2021

2,334 papers

Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks

Robert Lieck, Martin Rohrmeier

How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

Xinshuai Dong, Anh Tuan Luu, Min Lin, Shuicheng Yan, Hanwang Zhang

Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP

Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S Du

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

Sicheng Zhu, Bang An, Furong Huang

Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention

Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim

Efficient Bayesian network structure learning via local Markov boundary search

Ming Gao, Bryon Aragam

Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons

Wenbo Ren, Jia Liu, Ness Shroff

Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks

Chenning Yu, Sicun Gao

Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis

Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho

Learning to Learn Graph Topologies

Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias

Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A Erdogdu

Improving Robustness using Generated Data

Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian

A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models

Severi Rissanen, Pekka Marttinen

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Chongjian GE, Youwei Liang, Yibing Song, Jianbo Jiao, Jue Wang

E(n) Equivariant Normalizing Flows

Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling

Optimizing Conditional Value-At-Risk of Black-Box Functions

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

Environment Generation for Zero-Shot Compositional Reinforcement Learning

Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari

Understanding Partial Multi-Label Learning via Mutual Information

Xiuwen Gong, Dong Yuan, Wei Bao

Efficient Training of Retrieval Models using Negative Cache

Erik Lindgren, Sashank Reddi, Ruiqi Guo, Sanjiv Kumar

Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit

Shoutik Mukherjee, Behtash Babadi

Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess

Gregory Clark

How Tight Can PAC-Bayes be in the Small Data Regime?

Andrew Foong, Wessel Bruinsma, David Burt, Richard Turner

Speedy Performance Estimation for Neural Architecture Search

Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk

Towards Instance-Optimal Offline Reinforcement Learning with Pessimism

Ming Yin, Yu-Xiang Wang

Private Non-smooth ERM and SCO in Subquadratic Steps

Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data

Dongmin Park, Hwanjun Song, Minseok Kim, Jae-Gil Lee

IQ-Learn: Inverse soft-Q Learning for Imitation

Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon

Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond

Maria-Florina F Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik

Rebounding Bandits for Modeling Satiation Effects

Liu Leqi, Fatma Kilinc Karzan, Zachary Lipton, Alan Montgomery

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang

Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity

Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, Liqing Zhang

CoAtNet: Marrying Convolution and Attention for All Data Sizes

Zihang Dai, Hanxiao Liu, Quoc V Le, Mingxing Tan

Invariant Causal Imitation Learning for Generalizable Policies

Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

Idan Kligvasser, Tamar Shaham, Yuval Bahat, Tomer Michaeli

Privately Learning Mixtures of Axis-Aligned Gaussians

Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw

A No-go Theorem for Robust Acceleration in the Hyperbolic Plane

Linus Hamilton, Ankur Moitra

Adaptable Agent Populations via a Generative Model of Policies

Kenneth Derek, Phillip Isola

Robust and differentially private mean estimation

Xiyang Liu, Weihao Kong, Sham Kakade, Sewoong Oh

Associative Memories via Predictive Coding

Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder

SSMF: Shifting Seasonal Matrix Factorization

Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi

Information Directed Reward Learning for Reinforcement Learning

David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations

Moshe Eliasof, Eldad Haber, Eran Treister

Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach

Qiujiang Jin, Aryan Mokhtari

On the Out-of-distribution Generalization of Probabilistic Image Modelling

Mingtian Zhang, Andi Zhang, Steven McDonagh

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness

Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou

Debiased Visual Question Answering from Feature and Sample Perspectives

Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, Qi Wu

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

Amrith Setlur, Oscar Li, Virginia Smith

Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration

Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu

Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components

Nate Veldt, Austin R Benson, Jon M. Kleinberg

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

Ke Sun, Yafei Wang, Yi Liu, yingnan zhao, Bo Pan

Learning Knowledge Graph-based World Models of Textual Environments

Prithviraj Ammanabrolu, Mark Riedl

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert

On Effective Scheduling of Model-based Reinforcement Learning

Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang

Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

Nicklas Hansen, Hao Su, Xiaolong Wang

Control Variates for Slate Off-Policy Evaluation

Nikos Vlassis, Ashok Chandrashekar, Fernando Amat, Nathan Kallus

The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations

Peter Hase, Harry Xie, Mohit Bansal

Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning

Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas Diggavi

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space

Taiji Suzuki, Atsushi Nitanda

Hypergraph Propagation and Community Selection for Objects Retrieval

Guoyuan An, Yuchi Huo, Sung-eui Yoon

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris

On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness

Eric Mintun, Alexander Kirillov, Saining Xie

Tractable Regularization of Probabilistic Circuits

Anji Liu, Guy Van den Broeck

Adversarial Robustness of Streaming Algorithms through Importance Sampling

Vladimir braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal

From global to local MDI variable importances for random forests and when they are Shapley values

Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

Patrick Esser, Robin Rombach, Andreas Blattmann, Bjorn Ommer

Neural Auto-Curricula in Two-Player Zero-Sum Games

Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen Mcaleer

Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)

Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne

RMM: Reinforced Memory Management for Class-Incremental Learning

Yaoyao Liu, Bernt Schiele, Qianru Sun

BayesIMP: Uncertainty Quantification for Causal Data Fusion

Siu Lun Chau, Jean-Francois Ton, Javier Gonzalez, Yee W. Teh, Dino Sejdinovic

Repulsive Deep Ensembles are Bayesian

Francesco D'Angelo, Vincent Fortuin

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio

Locally private online change point detection

Tom Berrett, Yi Yu

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D Rao

On the Theory of Reinforcement Learning with Once-per-Episode Feedback

Niladri Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan

Pooling by Sliced-Wasserstein Embedding

Navid Naderializadeh, Joseph F Comer, Reed Andrews, Heiko Hoffmann, Soheil Kolouri

Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks

Zichen Miao, Ze Wang, Xiuyuan Cheng, Qiang Qiu

Exact marginal prior distributions of finite Bayesian neural networks

Jacob Zavatone-Veth, Cengiz Pehlevan

The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

Geoff Pleiss, John P. Cunningham

Towards Lower Bounds on the Depth of ReLU Neural Networks

Christoph Hertrich, Amitabh Basu, Marco Di Summa, Martin Skutella

Learning Equilibria in Matching Markets from Bandit Feedback

Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt

Dangers of Bayesian Model Averaging under Covariate Shift

Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew G Wilson

A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering

Tom Hess, Michal Moshkovitz, Sivan Sabato

Looking Beyond Single Images for Contrastive Semantic Segmentation Learning

FEIHU ZHANG, Philip Torr, Rene Ranftl, Stephan Richter

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks

Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C Green

Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training

Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister

Exploring Forensic Dental Identification with Deep Learning

Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting Shao

An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders

Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin

VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media

Yizhou Zhang, Karishma Sharma, Yan Liu

Unsupervised Motion Representation Learning with Capsule Autoencoders

Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S Kankanhalli

Statistical Query Lower Bounds for List-Decodable Linear Regression

Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes

Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

AutoBalance: Optimized Loss Functions for Imbalanced Data

Mingchen Li, Xuechen Zhang, CHRISTOS THRAMPOULIDIS, Jiasi Chen, Samet Oymak

Can Information Flows Suggest Targets for Interventions in Neural Circuits?

Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover

Jianbo Ouyang, Hui Wu, Min Wang, Wengang Zhou, Houqiang Li

Framing RNN as a kernel method: A neural ODE approach

Adeline Fermanian, Pierre Marion, Jean-philippe Vert, Gérard Biau

3D Pose Transfer with Correspondence Learning and Mesh Refinement

Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin

On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning

Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman Ozdaglar

Hyperparameter Optimization Is Deceiving Us, and How to Stop It

A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher M De Sa

Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks

Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener