ICML 2017

434 papers

Online Learning to Rank in Stochastic Click Models

Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári

Recurrent Highway Networks

Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnı́k, Jürgen Schmidhuber

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm

Rongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, ℓ_2-consistency and Neuroscience Applications

Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba

Identify the Nash Equilibrium in Static Games with Random Payoffs

Yichi Zhou, Jialian Li, Jun Zhu

Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values

Chaoxu Zhou, Wenbo Gao, Donald Goldfarb

Recovery Guarantees for One-hidden-layer Neural Networks

Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon

Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible

Kai Zheng, Wenlong Mou, Liwei Wang

Asynchronous Stochastic Gradient Descent with Delay Compensation

Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu

Follow the Moving Leader in Deep Learning

Shuai Zheng, James T. Kwok

Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture

Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi

Learning Hierarchical Features from Deep Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang

Leveraging Node Attributes for Incomplete Relational Data

He Zhao, Lan Du, Wray Buntine

Multi-Class Optimal Margin Distribution Machine

Teng Zhang, Zhi-Hua Zhou

Projection-free Distributed Online Learning in Networks

Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang

Convexified Convolutional Neural Networks

Yuchen Zhang, Percy Liang, Martin J. Wainwright

ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu

Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method

Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T-H. Hubert Chan

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction

Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai

Adversarial Feature Matching for Text Generation

Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao

Stochastic Gradient Monomial Gamma Sampler

Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin

Continual Learning Through Synaptic Intelligence

Friedemann Zenke, Ben Poole, Surya Ganguli

Canopy Fast Sampling with Cover Trees

Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola

Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data

Manzil Zaheer, Amr Ahmed, Alexander J. Smola

Combined Group and Exclusive Sparsity for Deep Neural Networks

Jaehong Yoon, Sung Ju Hwang

Latent Feature Lasso

Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin

A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization

Jianbo Ye, James Z. Wang, Jia Li

Approximate Newton Methods and Their Local Convergence

Haishan Ye, Luo Luo, Zhihua Zhang

Scalable Bayesian Rule Lists

Hongyu Yang, Cynthia Rudin, Margo Seltzer

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity

Eunho Yang, Aurelie C. Lozano

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates

Tianbao Yang, Qihang Lin, Lijun Zhang

Tensor-Train Recurrent Neural Networks for Video Classification

Yinchong Yang, Denis Krompass, Volker Tresp

Improved Variational Autoencoders for Text Modeling using Dilated Convolutions

Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick

On The Projection Operator to A Three-view Cardinality Constrained Set

Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong

High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation

Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu

Adaptive Consensus ADMM for Distributed Optimization

Zheng Xu, Gavin Taylor, Hao Li, Mario A. T. Figueiredo, Xiaoming Yuan

Learning Hawkes Processes from Short Doubly-Censored Event Sequences

Hongteng Xu, Dixin Luo, Hongyuan Zha

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence

Yi Xu, Qihang Lin, Tianbao Yang

Uncorrelation and Evenness: a New Diversity-Promoting Regularizer

Pengtao Xie, Aarti Singh, Eric P. Xing

Learning Latent Space Models with Angular Constraints

Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu

Dual Supervised Learning

Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu

A Unified View of Multi-Label Performance Measures

Xi-Zhu Wu, Zhi-Hua Zhou

Tensor Belief Propagation

Andrew Wrigley, Wee Sun Lee, Nan Ye

Exact Inference for Integer Latent-Variable Models

Kevin Winner, Debora Sujono, Dan Sheldon

Learned Optimizers that Scale and Generalize

Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil

Unifying Task Specification in Reinforcement Learning

Martha White

Latent Intention Dialogue Models

Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve Young

Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression

Pengfei Wei, Ramon Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery

Lingxiao Wang, Xiao Zhang, Quanquan Gu

Beyond Filters: Compact Feature Map for Portable Deep Model

Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao

Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms

Jialei Wang, Lin Xiao

Variational Policy for Guiding Point Processes

Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song

Sequence Modeling via Segmentations

Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou

Tensor Decomposition via Simultaneous Power Iteration

Po-An Wang, Chi-Jen Lu

Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli

Robust Probabilistic Modeling with Bayesian Data Reweighting

Yixin Wang, Alp Kucukelbir, David M. Blei

Efficient Distributed Learning with Sparsity

Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang

Max-value Entropy Search for Efficient Bayesian Optimization

Zi Wang, Stefanie Jegelka

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption

Lingxiao Wang, Quanquan Gu

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging

Shusen Wang, Alex Gittens, Michael W. Mahoney

Capacity Releasing Diffusion for Speed and Locality

Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits

Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudı́k

Fast Bayesian Intensity Estimation for the Permanental Process

Christian J. Walder, Adrian N. Bishop

On orthogonality and learning recurrent networks with long term dependencies

Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

Learning to Generate Long-term Future via Hierarchical Prediction

Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation

Carlos Villacampa-Calvo, Daniel Hernández-lobato

FeUdal Networks for Hierarchical Reinforcement Learning

Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg

Model-Independent Online Learning for Influence Maximization

Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan

Automatic Discovery of the Statistical Types of Variables in a Dataset

Isabel Valera, Zoubin Ghahramani

Learning Determinantal Point Processes with Moments and Cycles

John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet

Learning Stable Stochastic Nonlinear Dynamical Systems

Jonas Umlauft, Sandra Hirche

Multilabel Classification with Group Testing and Codes

Shashanka Ubaru, Arya Mazumdar

Breaking Locality Accelerates Block Gauss-Seidel

Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan

Hyperplane Clustering via Dual Principal Component Pursuit

Manolis C. Tsakiris, René Vidal

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song

Magnetic Hamiltonian Monte Carlo

Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

Diameter-Based Active Learning

Christopher Tosh, Sanjoy Dasgupta

Boosted Fitted Q-Iteration

Samuele Tosatto, Matteo Pirotta, Carlo D’Eramo, Marcello Restelli

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Evaluating the Variance of Likelihood-Ratio Gradient Estimators

Seiya Tokui, Issei Sato

An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis

Yuandong Tian

Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification

Hoai An Le Thi, Hoai Minh Le, Duy Nhat Phan, Bach Tran

Neural Networks and Rational Functions

Matus Telgarsky

Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares

Junqi Tang, Mohammad Golbabaee, Mike E. Davies

Gradient Coding: Avoiding Stragglers in Distributed Learning

Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis

Partitioned Tensor Factorizations for Learning Mixed Membership Models

Zilong Tan, Sayan Mukherjee

Coherent Probabilistic Forecasts for Hierarchical Time Series

Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman

Selective Inference for Sparse High-Order Interaction Models

Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi

Distributed Mean Estimation with Limited Communication

Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

Axiomatic Attribution for Deep Networks

Mukund Sundararajan, Ankur Taly, Qiqi Yan

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang

Relative Fisher Information and Natural Gradient for Learning Large Modular Models

Ke Sun, Frank Nielsen

Safety-Aware Algorithms for Adversarial Contextual Bandit

Wen Sun, Debadeepta Dey, Ashish Kapoor

Tensor Balancing on Statistical Manifold

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Ordinal Graphical Models: A Tale of Two Approaches

Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar

Approximate Steepest Coordinate Descent

Sebastian U. Stich, Anant Raj, Martin Jaggi

Probabilistic Submodular Maximization in Sub-Linear Time

Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi