NeurIPS 2021

2,334 papers

Online Meta-Learning via Learning with Layer-Distributed Memory

Sudarshan Babu, Pedro Savarese, Michael Maire

Data driven semi-supervised learning

Maria-Florina F Balcan, Dravyansh Sharma

Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent

Rishav Chourasia, Jiayuan Ye, Reza Shokri

A Central Limit Theorem for Differentially Private Query Answering

Jinshuo Dong, Weijie Su, Linjun Zhang

TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

Yifan Jiang, Shiyu Chang, Zhangyang Wang

MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents

Stephen Chung

Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent

Xingchao Liu, Xin Tong, Qiang Liu

Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights

Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos

Hard-Attention for Scalable Image Classification

Athanasios Papadopoulos, Pawel Korus, Nasir Memon

Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, Hengshu Zhu

Sequential Causal Imitation Learning with Unobserved Confounders

Daniel Kumor, Junzhe Zhang, Elias Bareinboim

What Matters for Adversarial Imitation Learning?

Manu Orsini, Anton Raichuk, Leonard Hussenot, Damien Vincent, Robert Dadashi

Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares

Hiroaki Yamada, Makoto Yamada

Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning

Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man (Man) Cheung

On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms

Shuyu Cheng, Guoqiang Wu, Jun Zhu

Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani

Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark

Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M Solomon, Alexander Filippov

Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification

Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

Mathias Niepert, Pasquale Minervini, Luca Franceschi

S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks

Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing XU

Controlled Text Generation as Continuous Optimization with Multiple Constraints

Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov

Online Selective Classification with Limited Feedback

Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama

Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin

Collaborating with Humans without Human Data

DJ Strouse, Kevin McKee, Matt Botvinick, Edward Hughes, Richard Everett

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer

Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos

Reuben Tan, Bryan Plummer, Kate Saenko, Hailin Jin, Bryan Russell

Bayesian Optimization of Function Networks

Raul Astudillo, Peter Frazier

Modified Frank Wolfe in Probability Space

Carson Kent, JIAJIN LI, Jose Blanchet, Peter W. Glynn, Peter W Glynn

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Oliver Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt

A PAC-Bayes Analysis of Adversarial Robustness

Paul Viallard, Eric Guillaume VIDOT, Amaury Habrard, Emilie Morvant

Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters

Arvind Mahankali, David Woodruff

Rethinking the Variational Interpretation of Accelerated Optimization Methods

Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand

Spot the Difference: Detection of Topological Changes via Geometric Alignment

Per Steffen Czolbe, Aasa Feragen, Oswin Krause

Credal Self-Supervised Learning

Julian Lienen, Eyke Hüllermeier

Improving Calibration through the Relationship with Adversarial Robustness

Yao Qin, Xuezhi Wang, Alex Beutel, Ed Chi

Fair Clustering Under a Bounded Cost

Seyed Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson

Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization

Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu

Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems

Zixiu Wang, Yiwen Guo, Hu Ding

Class-Incremental Learning via Dual Augmentation

Fei Zhu, Zhen Cheng, Xu-yao Zhang, Cheng-lin Liu

Last-iterate Convergence in Extensive-Form Games

Chung-Wei Lee, Christian Kroer, Haipeng Luo

Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation

Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi

Unsupervised Foreground Extraction via Deep Region Competition

Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu

Co-evolution Transformer for Protein Contact Prediction

He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao

BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation

Mingguo He, Zhewei Wei, zengfeng Huang, Hongteng Xu

On the Convergence of Step Decay Step-Size for Stochastic Optimization

Xiaoyu Wang, Sindri Magnússon, Mikael Johansson

Convergence of adaptive algorithms for constrained weakly convex optimization

Ahmet Alacaoglu, Yura Malitsky, Volkan Cevher

Attention Bottlenecks for Multimodal Fusion

Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation

Jiafan He, Dongruo Zhou, Quanquan Gu

Compressed Video Contrastive Learning

Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu

A sampling-based circuit for optimal decision making

Camille Rullán Buxó, Cristina Savin

Optimizing Reusable Knowledge for Continual Learning via Metalearning

Julio Hurtado, Alain Raymond, Alvaro Soto

Posterior Meta-Replay for Continual Learning

Christian Henning, Maria Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber

Meta Learning Backpropagation And Improving It

Louis Kirsch, Jürgen Schmidhuber

Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang

Generalized DataWeighting via Class-Level Gradient Manipulation

Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue (Steve) Liu

Deep Extrapolation for Attribute-Enhanced Generation

Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik

Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics

Bhavin Choksi, Milad Mozafari, Callum Biggs O'May, B. ADOR, Andrea Alamia

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

Zihang Meng, Rudrasis Chakraborty, Vikas Singh

CCVS: Context-aware Controllable Video Synthesis

Guillaume Le Moing, Jean Ponce, Cordelia Schmid

From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits

Dorian Baudry, Patrick Saux, Odalric-ambrym Maillard

Lossy Compression for Lossless Prediction

Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J Maddison

Learning Treatment Effects in Panels with General Intervention Patterns

Vivek Farias, Andrew Li, Tianyi Peng

CLIP-It! Language-Guided Video Summarization

Medhini Narasimhan, Anna Rohrbach, Trevor Darrell

Exponential Graph is Provably Efficient for Decentralized Deep Training

Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, PAN PAN

PortaSpeech: Portable and High-Quality Generative Text-to-Speech

Yi Ren, Jinglin Liu, Zhou Zhao

Learning Transferable Adversarial Perturbations

Krishna kanth Nakka, Mathieu Salzmann

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou

Towards Biologically Plausible Convolutional Networks

Roman Pogodin, Yash Mehta, Timothy Lillicrap, Peter E. Latham, Peter E Latham

Dimension-free empirical entropy estimation

Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoﬀrey Wolfer

Logarithmic Regret in Feature-based Dynamic Pricing

Jianyu Xu, Yu-Xiang Wang

Overparameterization Improves Robustness to Covariate Shift in High Dimensions

Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

Contrastive Active Inference

Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt

Safe Reinforcement Learning by Imagining the Near Future

Garrett Thomas, Yuping Luo, Tengyu Ma

Prior-independent Dynamic Auctions for a Value-maximizing Buyer

Yuan Deng, Hanrui Zhang

ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation

Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen

Implicit Semantic Response Alignment for Partial Domain Adaptation

Wenxiao Xiao, Zhengming Ding, Hongfu Liu

Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features

Thomas McDonald, Mauricio Alvarez

Safe Reinforcement Learning with Natural Language Constraints

Tsung-Yen Yang, Michael Y Hu, Yinlam Chow, Karthik Narasimhan, Peter J. Ramadge

Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers

Mikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson

CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression

Zhize Li, Peter Richtarik

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles

Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen

Neural Routing by Memory

Kaipeng Zhang, Zhenqiang Li, Zhifeng Li, Wei Liu, Yoichi Sato

A Continuous Mapping For Augmentation Design

Keyu Tian, CHEN LIN, Ser Nam Lim, Wanli Ouyang, Puneet Dokania

Test-time Collective Prediction

Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan

Tree in Tree: from Decision Trees to Decision Graphs

Bingzhao Zhu, Mahsa Shoaran

Self-Supervised Multi-Object Tracking with Cross-input Consistency

Favyen Bastani, Songtao He, Samuel Madden

Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction

Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J Kim

Provably Faster Algorithms for Bilevel Optimization

Junjie Yang, Kaiyi Ji, Yingbin Liang

Uncertainty Calibration for Ensemble-Based Debiasing Methods

Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Cheng, Zhi-ming Ma

Bayesian Bellman Operators

Mattie Fellows, Kristian Hartikainen, Shimon Whiteson

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning

Andrea Zanette, Emma Brunskill, Martin J. Wainwright, Martin J Wainwright

Marginalised Gaussian Processes with Nested Sampling

Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen

Revisiting Smoothed Online Learning

Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang

Temporal-attentive Covariance Pooling Networks for Video Recognition

Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu, Peihua Li

A novel notion of barycenter for probability distributions based on optimal weak mass transport

Elsa Cazelles, Felipe Tobar, Joaquin Fontbona

Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders

Konstantin Genin

Heuristic-Guided Reinforcement Learning

Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

Nonsmooth Implicit Differentiation for Machine-Learning and Optimization

Jérôme Bolte, Tam Le, Edouard Pauwels, Tony Silveti-Falls

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints

Tianhao Wang, Dongruo Zhou, Quanquan Gu

Reverse-Complement Equivariant Networks for DNA Sequences

Vincent Mallet, Jean-philippe Vert