ICML 2018

621 papers

Hierarchical Long-term Video Prediction without Supervision

Nevan Wichers, Ruben Villegas, Dumitru Erhan, Honglak Lee

Rectify Heterogeneous Models with Semantic Mapping

Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou

Stochastic Variance-Reduced Hamilton Monte Carlo Methods

Difan Zou, Pan Xu, Quanquan Gu

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen

Distributed Nonparametric Regression under Communication Constraints

Yuancheng Zhu, John Lafferty

Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors

Yichi Zhou, Jun Zhu, Jingwei Zhuo

Stochastic Variance-Reduced Cubic Regularized Newton Methods

Dongruo Zhou, Pan Xu, Quanquan Gu

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

Kaiwen Zhou, Fanhua Shang, James Cheng

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?

Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye

Understanding Generalization and Optimization Performance of Deep CNNs

Pan Zhou, Jiashi Feng

Revealing Common Statistical Behaviors in Heterogeneous Populations

Andrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli

A Robust Approach to Sequential Information Theoretic Planning

Sue Zheng, Jason Pacheco, John Fisher

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Shuai Zheng, James Tin-Yau Kwok

Composite Marginal Likelihood Methods for Random Utility Models

Zhibing Zhao, Lirong Xia

MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning

Bo Zhao, Xinwei Sun, Yanwei Fu, Yuan Yao, Yizhou Wang

Adversarially Regularized Autoencoders

Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, Yann LeCun

Inter and Intra Topic Structure Learning with Word Embeddings

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Dynamic Regret of Strongly Adaptive Methods

Lijun Zhang, Tianbao Yang, jin, Zhi-Hua Zhou

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery

Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu

Noisy Natural Gradient as Variational Inference

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse

Composable Planning with Attributes

Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, arthur szlam, Rob Fergus

Deep Bayesian Nonparametric Tracking

Aonan Zhang, John Paisley

Tropical Geometry of Deep Neural Networks

Liwen Zhang, Gregory Naitzat, Lek-Heng Lim

Learning Long Term Dependencies via Fourier Recurrent Units

Jiong Zhang, Yibo Lin, Zhao Song, Inderjit Dhillon

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

Jiong Zhang, Qi Lei, Inderjit Dhillon

Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

Xueru Zhang, Mohammad Mahdi Khalili, mingyan liu

Safe Element Screening for Submodular Function Minimization

Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang

High Performance Zero-Memory Overhead Direct Convolutions

Jiyuan Zhang, Franz Franchetti, Tze Meng Low

Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion

Richard Zhang, Salar Fattahi, Somayeh Sojoudi

Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow

Xiao Zhang, Simon Du, Quanquan Gu

Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs

Andrea Zanette, Emma Brunskill

Policy Optimization as Wasserstein Gradient Flows

Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher

An Efficient Semismooth Newton based Algorithm for Convex Clustering

Yancheng Yuan, Defeng Sun, Kim-Chuan Toh

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, Jure Leskovec

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

Jinsung Yoon, James Jordon, Mihaela Schaar

GAIN: Missing Data Imputation using Generative Adversarial Nets

Jinsung Yoon, James Jordon, Mihaela Schaar

Probably Approximately Metric-Fair Learning

Gal Yona, Guy Rothblum

Disentangled Sequential Autoencoder

Li Yingzhen, Stephan Mandt

Semi-Implicit Variational Inference

Mingzhang Yin, Mingyuan Zhou

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

Loss Decomposition for Fast Learning in Large Output Spaces

Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar

Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach

Mao Ye, Yan Sun

Communication-Computation Efficient Gradient Coding

Min Ye, Emmanuel Abbe

Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under ℓ_p Distances

Grigory Yaroslavtsev, Adithya Vadapalli

Hierarchical Text Generation and Planning for Strategic Dialogue

Denis Yarats, Mike Lewis

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

Mean Field Multi-Agent Reinforcement Learning

Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang

Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy

Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville

Dependent Relational Gamma Process Models for Longitudinal Networks

Sikun Yang, Heinz Koeppl

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

Karren Yang, Abigail Katcoff, Caroline Uhler

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Bowei Yan, Sanmi Koyejo, Kai Zhong, Pradeep Ravikumar

Active Learning with Logged Data

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Causal Bandits with Propagating Inference

Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Broeck

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions

Pan Xu, Tianhao Wang, Quanquan Gu

Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data

Ganggang Xu, Zuofeng Shang, Guang Cheng

Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

Learning to Explore via Meta-Policy Gradient

Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

Representation Learning on Graphs with Jumping Knowledge Networks

Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-Ichi Kawarabayashi

Learning Registered Point Processes from Idiosyncratic Observations

Hongteng Xu, Lawrence Carin, Hongyuan Zha

Rates of Convergence of Spectral Methods for Graphon Estimation

Jiaming Xu

Learning Semantic Representations for Unsupervised Domain Adaptation

Shaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen

Nonoverlap-Promoting Variable Selection

Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing

Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis

Pengtao Xie, Wei Wu, Yichen Zhu, Eric Xing

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Lechao Xiao, Yasaman Bahri, Jascha Sohl-dickstein, Samuel Schoenholz, Jeffrey Pennington

Model-Level Dual Learning

Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu

Bayesian Quadrature for Multiple Related Integrals

Xiaoyue Xi, François-Xavier Briol, Mark Girolami

Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions

Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan

Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization

Hang Wu, May Wang

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training

Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha

Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization

Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang

SQL-Rank: A Listwise Approach to Collaborative Ranking

Liwei Wu, Cho-jui Hsieh, James Sharpnack

Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

Huasen Wu, Xueying Guo, Xin Liu

Local Density Estimation in High Dimensions

Xian Wu, Moses Charikar, Vishnu Natchu

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope

Eric Wong, Zico Kolter

Towards Fast Computation of Certified Robustness for ReLU Networks

Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-jui Hsieh

Deep Predictive Coding Network for Object Recognition

Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello

LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, András György, Csaba Szepesvári

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

Gail Weiss, Yoav Goldberg, Eran Yahav

Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

Daphna Weinshall, Gad Cohen, Dan Amir

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Stein Variational Message Passing for Continuous Graphical Models

Dilin Wang, Zhe Zeng, Qiang Liu

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. Ni

Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates

xue wang, Mingcheng Wei, Tao Yao

Adversarial Distillation of Bayesian Neural Network Posteriors

Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ-Skerry Ryan, Eric Battenberg

Provable Variable Selection for Streaming Features

Jing Wang, Jie Shen, Ping Li

A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

Beilun Wang, Arshdeep Sekhon, Yanjun Qi

Coded Sparse Matrix Multiplication

Sinong Wang, Jiashang Liu, Ness Shroff

Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations

Xingyu Wang, Diego Klabjan

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Yizhen Wang, Somesh Jha, Kamalika Chaudhuri

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, Philip S Yu

Thompson Sampling for Combinatorial Semi-Bandits

Siwei Wang, Wei Chen

Neural Dynamic Programming for Musical Self Similarity

Christian Walder, Dongwoo Kim

Semi-Supervised Learning on Data Streams via Temporal Label Propagation

Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra

Transfer Learning via Learning to Transfer

Ying Wei, Yu Zhang, Junzhou Huang, Qiang Yang

Hierarchical Multi-Label Classification Networks

Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros