ICML 2019

773 papers

Natural Analysts in Adaptive Data Analysis

Tijana Zrnic, Moritz Hardt

Fast Context Adaptation via Meta-Learning

Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whiteson

Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously

Julian Zimmert, Haipeng Luo, Chen-Yu Wei

Latent Normalizing Flows for Discrete Sequences

Zachary Ziegler, Alexander Rush

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization

Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects

Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

Learning Classifiers for Target Domain with Limited or No Labels

Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

Poission Subsampled Rényi Differential Privacy

Yuqing Zhu, Yu-Xiang Wang

Improved Dynamic Graph Learning through Fault-Tolerant Sparsification

Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, Jinbo Bi

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer

BayesNAS: A Bayesian Approach for Neural Architecture Search

Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan

Toward Understanding the Importance of Noise in Training Neural Networks

Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou

Lipschitz Generative Adversarial Nets

Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization

Dongruo Zhou, Quanquan Gu

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

Baojian Zhou, Feng Chen, Yiming Ying

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

Rui Zhao, Xudong Sun, Volker Tresp

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, Zhiru Zhang

Metric-Optimized Example Weights

Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta

On Learning Invariant Representations for Domain Adaptation

Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey Gordon

Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits

Martin Zhang, James Zou, David Tse

Interpreting Adversarially Trained Convolutional Neural Networks

Tianyuan Zhang, Zhanxing Zhu

Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization

Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang

Learning Novel Policies For Tasks

Yunbo Zhang, Wenhao Yu, Greg Turk

Theoretically Principled Trade-off between Robustness and Accuracy

Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui

Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models

Chenyang Zhang, Guosheng Yin

A Composite Randomized Incremental Gradient Method

Junyu Zhang, Lin Xiao

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson

Co-Representation Network for Generalized Zero-Shot Learning

Fei Zhang, GUANGMING Shi

Random Function Priors for Correlation Modeling

Aonan Zhang, John Paisley

Adaptive Regret of Convex and Smooth Functions

Lijun Zhang, Tie-yan Liu, Zhi-Hua Zhou

Bridging Theory and Algorithm for Domain Adaptation

Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan

Incremental Randomized Sketching for Online Kernel Learning

Xiao Zhang, Shizhong Liao

Neural Collaborative Subspace Clustering

Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li

LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

Songyang Zhang, Xuming He, Shipeng Yan

Circuit-GNN: Graph Neural Networks for Distributed Circuit Design

Guo Zhang, Hao He, Dina Katabi

Self-Attention Generative Adversarial Networks

Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena

When Samples Are Strategically Selected

Hanrui Zhang, Yu Cheng, Vincent Conitzer

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Chicheng Zhang, Alekh Agarwal, Hal Daume III, John Langford, Sahand Negahban

Making Convolutional Networks Shift-Invariant Again

Richard Zhang

Global Convergence of Block Coordinate Descent in Deep Learning

Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Andrea Zanette, Emma Brunskill

Context-Aware Zero-Shot Learning for Object Recognition

Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Alp Yurtsever, Suvrit Sra, Volkan Cevher

A Conditional-Gradient-Based Augmented Lagrangian Framework

Alp Yurtsever, Olivier Fercoq, Volkan Cevher

Dirichlet Simplex Nest and Geometric Inference

Mikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyen

Bayesian Nonparametric Federated Learning of Neural Networks

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang

Trimming the ℓ_1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning

Jihun Yun, Peng Zheng, Eunho Yang, Aurélie Lozano, Aleksandr Aravkin

Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory

Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun

Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation

Jinyang Yuan, Bin Li, Xiangyang Xue

Online Adaptive Principal Component Analysis and Its extensions

Jianjun Yuan, Andrew Lamperski

Distributed Learning over Unreliable Networks

Chen Yu, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla

Multi-Agent Adversarial Inverse Reinforcement Learning

Lantao Yu, Jiaming Song, Stefano Ermon

On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

Hao Yu, Rong Jin, Sen Yang

On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization

Hao Yu, Rong Jin

How does Disagreement Help Generalization against Label Corruption?

Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Yue Yu, Jie Chen, Tian Gao, Mo Yu

Learning Neurosymbolic Generative Models via Program Synthesis

Halley Young, Osbert Bastani, Mayur Naik

Position-aware Graph Neural Networks

Jiaxuan You, Rex Ying, Jure Leskovec

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

Sung Whan Yoon, Jun Seo, Jaekyun Moon

NAS-Bench-101: Towards Reproducible Neural Architecture Search

Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables

Mingzhang Yin, Yuguang Yue, Mingyuan Zhou

Rademacher Complexity for Adversarially Robust Generalization

Dong Yin, Ramchandran Kannan, Peter Bartlett

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

Understanding Geometry of Encoder-Decoder CNNs

Jong Chul Ye, Woon Kyoung Sung

Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k-means Clustering

Taisuke Yasuda, David Woodruff, Manuel Fernandez

Hierarchically Structured Meta-learning

Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations

Quanming Yao, James Tin-Yau Kwok, Bo Han

ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu

SWALP : Stochastic Weight Averaging in Low Precision Training

Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson

LegoNet: Efficient Convolutional Neural Networks with Lego Filters

Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing XU

Sample-Optimal Parametric Q-Learning Using Linearly Additive Features

Lin Yang, Mengdi Wang

Learning to Prove Theorems via Interacting with Proof Assistants

Kaiyu Yang, Jia Deng

Supervised Hierarchical Clustering with Exponential Linkage

Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum

Variational Russian Roulette for Deep Bayesian Nonparametrics

Kai Xu, Akash Srivastava, Charles Sutton

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence

Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Duke

Power k-Means Clustering

Jason Xu, Kenneth Lange

Calibrated Approximate Bayesian Inference

Hanwen Xing, Geoff Nicholls, Jeong Lee

Differentiable Linearized ADMM

Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

Cong Xie, Sanmi Koyejo, Indranil Gupta

On Scalable and Efficient Computation of Large Scale Optimal Transport

Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton

Simplifying Graph Convolutional Networks

Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu

Deep Compressed Sensing

Yan Wu, Mihaela Rosca, Timothy Lillicrap

Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin

Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice

Imitation Learning from Imperfect Demonstration

Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

Eric Wong, Frank Schmidt, Zico Kolter

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen

Fairness risk measures

Robert Williamson, Aditya Menon

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin

Moment-Based Variational Inference for Markov Jump Processes

Christian Wildner, Heinz Koeppl

Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth

Jacob Whitehill, Anand Ramakrishnan

Improving Model Selection by Employing the Test Data

Max Westphal, Werner Brannath

Learning deep kernels for exponential family densities

Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach

Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy

Non-Monotonic Sequential Text Generation

Sean Welleck, Kianté Brantley, Hal Daume III, Kyunghyun Cho

CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, András György, Csaba Szepesvári