ICML 2020

1,084 papers

Transformer Hawkes Process

Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha

A general recurrent state space framework for modeling neural dynamics during decision-making

David Zoltowski, Jonathan Pillow, Scott Linderman

Influenza Forecasting Framework based on Gaussian Processes

Christoph Zimmer, Reza Yaesoubi

Laplacian Regularized Few-Shot Learning

Imtiaz Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed

Learning Optimal Tree Models under Beam Search

Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li

Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE

Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, sekhar tatikonda, Xenophon Papademetris

When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment

Feng Zhu, Zeyu Zheng

Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming

Daoli Zhu, lei zhao

Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

Sicheng Zhu, Xiao Zhang, David Evans

Thompson Sampling Algorithms for Mean-Variance Bandits

Qiuyu Zhu, Vincent Tan

Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health

Liangyu Zhu, Wenbin Lu, Rui Song

Variance Reduction and Quasi-Newton for Particle-Based Variational Inference

Michael Zhu, Chang Liu, Jun Zhu

Robust Outlier Arm Identification

Yinglun Zhu, Sumeet Katariya, Robert Nowak

Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization

Pan Zhou, Xiao-Tong Yuan

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support

Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth

Time-Consistent Self-Supervision for Semi-Supervised Learning

Tianyi Zhou, Shengjie Wang, Jeff Bilmes

Nonparametric Score Estimators

Yuhao Zhou, Jiaxin Shi, Jun Zhu

MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time

Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi

Neural Contextual Bandits with UCB-based Exploration

Dongruo Zhou, Lihong Li, Quanquan Gu

Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting

Zixin Zhong, Wang Chi Cheung, Vincent Tan

Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer

Anton Zhiyanov, Alexey Drutsa

Robust Graph Representation Learning via Neural Sparsification

Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni

Error-Bounded Correction of Noisy Labels

Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas

What Can Learned Intrinsic Rewards Capture?

Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss

Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion

Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie Su

Learning to Learn Kernels with Variational Random Features

Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin

Smaller, more accurate regression forests using tree alternating optimization

Arman Zharmagambetov, Miguel Carreira-Perpinan

Individual Calibration with Randomized Forecasting

Shengjia Zhao, Tengyu Ma, Stefano Ermon

Feature Quantization Improves GAN Training

Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen

Do RNN and LSTM have Long Memory?

Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin

On Learning Language-Invariant Representations for Universal Machine Translation

Han Zhao, Junjie Hu, Andrej Risteski

On Leveraging Pretrained GANs for Generation with Limited Data

Miaoyun Zhao, Yulai Cong, Lawrence Carin

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter Liu

Learning with Feature and Distribution Evolvable Streams

Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou

Variance Reduction in Stochastic Particle-Optimization Sampling

Jianyi Zhang, Yang Zhao, Changyou Chen

Perceptual Generative Autoencoders

Zijun Zhang, Ruixiang ZHANG, Zongpeng Li, Yoshua Bengio, Liam Paull

A Flexible Latent Space Model for Multilayer Networks

Xuefei Zhang, Songkai Xue, Ji Zhu

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Jingfeng ZHANG, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case

SHUAI ZHANG, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong

Sparsified Linear Programming for Zero-Sum Equilibrium Finding

Brian Zhang, Tuomas Sandholm

Convex Calibrated Surrogates for the Multi-Label F-Measure

Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Wei Zhang, Thomas Panum, Somesh Jha, Prasad Chalasani, David Page

Adaptive Reward-Poisoning Attacks against Reinforcement Learning

Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu

Invariant Causal Prediction for Block MDPs

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation

Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

Shangtong Zhang, Bo Liu, Shimon Whiteson

Self-Attentive Hawkes Process

Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions

Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

Optimal Estimator for Unlabeled Linear Regression

Hang Zhang, Ping Li

Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective

Ruixiang ZHANG, Masanori Koyama, Katsuhiko Ishiguro

Privately Learning Markov Random Fields

Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Steven Wu

Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han

Spread Divergence

Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

Richard Zhang, Daniel Golovin

Approximation Capabilities of Neural ODEs and Invertible Residual Networks

Han Zhang, Xi Gao, Jacob Unterman, Tom Arodz

A Tree-Structured Decoder for Image-to-Markup Generation

Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei

Learning the Valuations of a k-demand Agent

Hanrui Zhang, Vincent Conitzer

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman

Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate

Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games

Youzhi Zhang, Bo An

Robustness to Programmable String Transformations via Augmented Abstract Training

Yuhao Zhang, Aws Albarghouthi, Loris D’Antoni

Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach

Junzhe Zhang

Learning Calibratable Policies using Programmatic Style-Consistency

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing

Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck

Learning Near Optimal Policies with Low Inherent Bellman Error

Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill

Graph Random Neural Features for Distance-Preserving Graph Representations

Daniele Zambon, Cesare Alippi, Lorenzo Livi

Training Deep Energy-Based Models with f-Divergence Minimization

Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

Federated Learning with Only Positive Labels

Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

Wenhui Yu, Zheng Qin

Intrinsic Reward Driven Imitation Learning via Generative Model

Xingrui Yu, Yueming LYU, Ivor Tsang

Label-Noise Robust Domain Adaptation

Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich

Graphical Models Meet Bandits: A Variational Thompson Sampling Approach

Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel

Simultaneous Inference for Massive Data: Distributed Bootstrap

Yang Yu, Shih-Kang Chao, Guang Cheng

Graph Structure of Neural Networks

Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie

When Does Self-Supervision Help Graph Convolutional Networks?

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

Robustifying Sequential Neural Processes

Jaesik Yoon, Gautam Singh, Sungjin Ahn

XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning

Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon

Data Valuation using Reinforcement Learning

Jinsung Yoon, Sercan Arik, Tomas Pfister

It’s Not What Machines Can Learn, It’s What We Cannot Teach

Gal Yehuda, Moshe Gabel, Assaf Schuster

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans

Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification

Hui Ye, Zhiyu Chen, Da-Han Wang, Brian Davison

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Michihiro Yasunaga, Percy Liang

Searching to Exploit Memorization Effect in Learning with Noisy Labels

Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks

Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks

Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma

Multi-Agent Determinantal Q-Learning

Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

Lin Yang, Mengdi Wang

Interpolation between Residual and Non-Residual Networks

Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi

On the consistency of top-k surrogate losses

Forest Yang, Sanmi Koyejo

Improving Molecular Design by Stochastic Iterative Target Augmentation

Kevin Yang, Wengong Jin, Kyle Swanson, Dr.Regina Barzilay, Tommi Jaakkola

Q-value Path Decomposition for Deep Multiagent Reinforcement Learning

Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen

Randomized Smoothing of All Shapes and Sizes

Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya Razenshteyn

Energy-Based Processes for Exchangeable Data

Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Variational Bayesian Quantization

Yibo Yang, Robert Bamler, Stephan Mandt

Stochastic Optimization for Non-convex Inf-Projection Problems

Yan Yan, Yi Xu, Lijun Zhang, Wang Xiaoyu, Tianbao Yang

Feature Selection using Stochastic Gates

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization

Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan Adams

Video Prediction via Example Guidance

Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell