ICML 2020

1,084 papers

MetaFun: Meta-Learning with Iterative Functional Updates

Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, Yee Whye Teh

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda

Variational Label Enhancement

Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng

Learning Factorized Weight Matrix for Joint Filtering

Xiangyu Xu, Yongrui Ma, Wenxiu Sun

Learning Autoencoders with Relational Regularization

Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin

Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory

Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang

A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation

Pan Xu, Quanquan Gu

Class-Weighted Classification: Trade-offs and Robust Approaches

Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar

On Variational Learning of Controllable Representations for Text without Supervision

Peng Xu, Jackie Chi Kit CHEUNG, Yanshuai Cao

On Layer Normalization in the Transformer Architecture

Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng

On the Number of Linear Regions of Convolutional Neural Networks

Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems

Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang

Zeno++: Robust Fully Asynchronous SGD

Cong Xie, Sanmi Koyejo, Indranil Gupta

Maximum-and-Concatenation Networks

Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu

Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing

Yuxuan Xie, Jilles Dibangoye, Olivier Buffet

Disentangling Trainability and Generalization in Deep Neural Networks

Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz

Generative Flows with Matrix Exponential

Changyi Xiao, Ligang Liu

A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning

Yunhua Xiang, Noah Simon

Continuous Graph Neural Networks

Louis-Pascal Xhonneux, Meng Qu, Jian Tang

Amortized Population Gibbs Samplers with Neural Sufficient Statistics

Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent

On the Generalization Effects of Linear Transformations in Data Augmentation

Sen Wu, Hongyang Zhang, Gregory Valiant, Christopher Re

Adversarial Robustness via Runtime Masking and Cleansing

Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu

Sequence Generation with Mixed Representations

Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Jian-Huang Lai

Stronger and Faster Wasserstein Adversarial Attacks

Kaiwen Wu, Allen Wang, Yaoliang Yu

On the Noisy Gradient Descent that Generalizes as SGD

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir braverman

DeltaGrad: Rapid retraining of machine learning models

Yinjun Wu, Edgar Dobriban, Susan Davidson

Obtaining Adjustable Regularization for Free via Iterate Averaging

Jingfeng Wu, Vladimir braverman, Lin Yang

Is Local SGD Better than Minibatch SGD?

Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins

Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling

David Woodruff, Amir Zandieh

Causal Inference using Gaussian Processes with Structured Latent Confounders

Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

Learning to Rank Learning Curves

Martin Wistuba, Tejaswini Pedapati

Efficiently sampling functions from Gaussian process posteriors

James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth

Efficient nonparametric statistical inference on population feature importance using Shapley values

Brian Williamson, Jean Feng

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

William Wilkinson, Paul Chang, Michael Andersen, Arno Solin

Predictive Sampling with Forecasting Autoregressive Models

Auke Wiggers, Emiel Hoogeboom

How Good is the Bayes Posterior in Deep Neural Networks Really?

Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran

Amortised Learning by Wake-Sleep

Li Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani

Towards Understanding the Regularization of Adversarial Robustness on Neural Networks

Yuxin Wen, Shuai Li, Kui Jia

Domain Aggregation Networks for Multi-Source Domain Adaptation

Junfeng Wen, Russell Greiner, Dale Schuurmans

Batch Stationary Distribution Estimation

Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans

Online Control of the False Coverage Rate and False Sign Rate

Asaf Weinstein, Aaditya Ramdas

The Implicit and Explicit Regularization Effects of Dropout

Colin Wei, Sham Kakade, Tengyu Ma

Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain

Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb

Learning Representations that Support Extrapolation

Taylor Webb, Zachary Dulberg, Steven Frankland, Alexander Petrov, Randall O’Reilly

A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model

Peng Wang, Zirui Zhou, Anthony Man-Cho So

Thompson Sampling via Local Uncertainty

Zhendong Wang, Mingyuan Zhou

On Lp-norm Robustness of Ensemble Decision Stumps and Trees

Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-jui Hsieh

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross

Cost-effectively Identifying Causal Effects When Only Response Variable is Observable

Tian-Zuo Wang, Xi-Zhu Wu, Sheng-jun Huang, Zhi-Hua Zhou

Neural Network Control Policy Verification With Persistent Adversarial Perturbation

Yuh-Shyang Wang, Lily Weng, Luca Daniel

Sequential Cooperative Bayesian Inference

Junqi Wang, Pei Wang, Patrick Shafto

Loss Function Search for Face Recognition

Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Qi Wang, Herke van Hoof

When deep denoising meets iterative phase retrieval

Yaotian Wang, Xiaohang Sun, Jason Fleischer

Bandits for BMO Functions

Tianyu Wang, Cynthia Rudin

Optimizing Data Usage via Differentiable Rewards

Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang, Arash PakBin, Bobak Mortazavi, Hongyu Zhao, Donald Lee

Deep Streaming Label Learning

Zhen Wang, Liu Liu, Dacheng Tao

Haar Graph Pooling

Yu Guang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang

Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Tongzhou Wang, Phillip Isola

Frustratingly Simple Few-Shot Object Detection

Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An

Continuously Indexed Domain Adaptation

Hao Wang, Hao He, Dina Katabi

Non-separable Non-stationary random fields

Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark Steel

ROMA: Multi-Agent Reinforcement Learning with Emergent Roles

Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang

Upper bounds for Model-Free Row-Sparse Principal Component Analysis

Guanyi Wang, Santanu Dey

Self-Modulating Nonparametric Event-Tensor Factorization

Zheng Wang, Xinqi Chu, Shandian Zhe

Towards Accurate Post-training Network Quantization via Bit-Split and Stitching

Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng

On the Global Optimality of Model-Agnostic Meta-Learning

Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Logistic Regression for Massive Data with Rare Events

HaiYing Wang

Projection-free Distributed Online Convex Optimization with O(√T) Communication Complexity

Yuanyu Wan, Wei-Wei Tu, Lijun Zhang

Orthogonalized SGD and Nested Architectures for Anytime Neural Networks

Chengcheng Wan, Henry Hoffmann, Shan Lu, Michael Maire

Safe Reinforcement Learning in Constrained Markov Decision Processes

Akifumi Wachi, Yanan Sui

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

Andrey Voynov, Artem Babenko

Conditional gradient methods for stochastically constrained convex minimization

Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher

New Oracle-Efficient Algorithms for Private Synthetic Data Release

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Steven Wu

Private Reinforcement Learning with PAC and Regret Guarantees

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu

Born-Again Tree Ensembles

Thibaut Vidal, Maximilian Schiffer

OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning

Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z Leibo

Non-Stationary Delayed Bandits with Intermediate Observations

Claire Vernade, András György, Timothy Mann

Linear bandits with Stochastic Delayed Feedback

Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis

Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik

Uncertainty Estimation Using a Single Deep Deterministic Neural Network

Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal

Undirected Graphical Models as Approximate Posteriors

Arash Vahdat, Evgeny Andriyash, William Macready

StochasticRank: Global Optimization of Scale-Free Discrete Functions

Aleksei Ustimenko, Liudmila Prokhorenkova

Minimax Weight and Q-Function Learning for Off-Policy Evaluation

Masatoshi Uehara, Jiawei Huang, Nan Jiang

Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network

Javier Turek, Shailee Jain, Vy Vo, Mihai Capotă, Alexander Huth

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis

Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry

Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources

Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho

GraphOpt: Learning Optimization Models of Graph Formation

Rakshit Trivedi, Jiachen Yang, Hongyuan Zha

Single Point Transductive Prediction

Nilesh Tripuraneni, Lester Mackey

Bayesian Differential Privacy for Machine Learning

Aleksei Triastcyn, Boi Faltings

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization

Quoc Tran-Dinh, Nhan Pham, Lam Nguyen

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

Csaba Toth, Harald Oberhauser