ICML 2017

434 papers

Measuring Sample Quality with Kernels

Jackson Gorham, Lester Mackey

Preferential Bayesian Optimization

Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

Convex Phase Retrieval without Lifting via PhaseMax

Tom Goldstein, Christoph Studer

Neural Message Passing for Quantum Chemistry

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl

On Context-Dependent Clustering of Bandits

Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella

Convolutional Sequence to Sequence Learning

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

Rong Ge, Chi Jin, Yi Zheng

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs

Guillaume Gautier, Rémi Bardenet, Michal Valko

Differentiable Programs with Neural Libraries

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis

Dan Garber, Ohad Shamir, Nathan Srebro

Local-to-Global Bayesian Network Structure Learning

Tian Gao, Kshitij Fadnis, Murray Campbell

Deep Bayesian Active Learning with Image Data

Yarin Gal, Riashat Islam, Zoubin Ghahramani

Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

Joseph Futoma, Sanjay Hariharan, Katherine Heller

Forward and Reverse Gradient-Based Hyperparameter Optimization

Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

Counterfactual Data-Fusion for Online Reinforcement Learners

Andrew Forney, Judea Pearl, Elias Bareinboim

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

Jakob N. Foerster, Justin Gilmer, Jascha Sohl-dickstein, Jan Chorowski, David Sussillo

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn, Pieter Abbeel, Sergey Levine

Coresets for Vector Summarization with Applications to Network Graphs

Dan Feldman, Sedat Ozer, Daniela Rus

Regret Minimization in Behaviorally-Constrained Zero-Sum Games

Gabriele Farina, Christian Kroer, Tuomas Sandholm

Fake News Mitigation via Point Process Based Intervention

Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi

Maximum Selection and Ranking under Noisy Comparisons

Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening

Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi

Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement

Jonathan Eckstein, Noam Goldberg, Ai Kagawa

Stochastic Variance Reduction Methods for Policy Evaluation

Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou

Dance Dance Convolution

Chris Donahue, Zachary C. Lipton, Julian McAuley

A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI

Justin Domke

Sharp Minima Can Generalize For Deep Nets

Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio

Probabilistic Path Hamiltonian Monte Carlo

Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV

Being Robust (in High Dimensions) Can Be Practical

Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra

RobustFill: Neural Program Learning under Noisy I/O

Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed

Image-to-Markup Generation with Coarse-to-Fine Attention

Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush

iSurvive: An Interpretable, Event-time Prediction Model for mHealth

Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson

Consistency Analysis for Binary Classification Revisited

Krzysztof Dembczynski, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan

Distributed Batch Gaussian Process Optimization

Erik A. Daxberger, Bryan Kian Hsiang Low

An Infinite Hidden Markov Model With Similarity-Biased Transitions

Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

Logarithmic Time One-Against-Some

Hal Daume III, Nikos Karampatziakis, John Langford, Paul Mineiro

Stochastic Generative Hashing

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

Understanding Synthetic Gradients and Decoupled Neural Interfaces

Wojciech Marian Czarnecki, Grzegorz Swirszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals

Soft-DTW: a Differentiable Loss Function for Time-Series

Marco Cuturi, Mathieu Blondel

Random Feature Expansions for Deep Gaussian Processes

Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou

Parseval Networks: Improving Robustness to Adversarial Examples

Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier

On Kernelized Multi-armed Bandits

Sayak Ray Chowdhury, Aditya Gopalan

Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution

Po-Wei Chou, Daniel Maturana, Sebastian Scherer

On Relaxing Determinism in Arithmetic Circuits

Arthur Choi, Adnan Darwiche

MEC: Memory-efficient Convolution for Deep Neural Network

Minsik Cho, Daniel Brand

Algorithms for ℓ_p Low-Rank Approximation

Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy

Nearly Optimal Robust Matrix Completion

Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain

Learning to Aggregate Ordinal Labels by Maximizing Separating Width

Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng Ann Heng

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability

Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao

Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data

Xixian Chen, Michael R. Lyu, Irwin King

Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables

Bryant Chen, Daniel Kumor, Elias Bareinboim

Learning to Learn without Gradient Descent by Gradient Descent

Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions

Yichen Chen, DongDong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye

Dueling Bandits with Weak Regret

Bangrui Chen, Peter I. Frazier

Adaptive Multiple-Arm Identification

Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou

Robust Structured Estimation with Single-Index Models

Sheng Chen, Arindam Banerjee

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal

Active Heteroscedastic Regression

Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan

Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference

Aditya Chaudhry, Pan Xu, Quanquan Gu

Multiple Clustering Views from Multiple Uncertain Experts

Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman

Sliced Wasserstein Kernel for Persistence Diagrams

Mathieu Carrière, Marco Cuturi, Steve OUDOT

“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Priv’IT: Private and Sample Efficient Identity Testing

Bryan Cai, Constantinos Daskalakis, Gautam Kamath

Multi-objective Bandits: Optimizing the Generalized Gini Index

Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs

Alon Brutzkus, Amir Globerson

Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning

Noam Brown, Tuomas Sandholm

On the Sampling Problem for Kernel Quadrature

François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami

Clustering High Dimensional Dynamic Data Streams

Vladimir braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang

Lazifying Conditional Gradient Algorithms

Gábor Braun, Sebastian Pokutta, Daniel Zink

Practical Gauss-Newton Optimisation for Deep Learning

Aleksandar Botev, Hippolyt Ritter, David Barber

Programming with a Differentiable Forth Interpreter

Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel

Compressed Sensing using Generative Models

Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis

Adaptive Neural Networks for Efficient Inference

Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama

Unsupervised Learning by Predicting Noise

Piotr Bojanowski, Armand Joulin

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Ilija Bogunovic, Slobodan Mitrovic, Jonathan Scarlett, Volkan Cevher

Guarantees for Greedy Maximization of Non-submodular Functions with Applications

Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek

Efficient Online Bandit Multiclass Learning with Õ(√T) Regret

Alina Beygelzimer, Francesco Orabona, Chicheng Zhang

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models

Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay

Learning Texture Manifolds with the Periodic Spatial GAN

Urs Bergmann, Nikolay Jetchev, Roland Vollgraf

Neural Optimizer Search with Reinforcement Learning

Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le

A Distributional Perspective on Reinforcement Learning

Marc G. Bellemare, Will Dabney, Rémi Munos

Learning to Discover Sparse Graphical Models

Eugene Belilovsky, Kyle Kastner, Gael Varoquaux, Matthew B. Blaschko

End-to-End Learning for Structured Prediction Energy Networks

David Belanger, Bishan Yang, Andrew McCallum

Globally Induced Forest: A Prepruning Compression Scheme

Jean-Michel Begon, Arnaud Joly, Pierre Geurts

Unimodal Probability Distributions for Deep Ordinal Classification

Christopher Beckham, Christopher Pal

Emulating the Expert: Inverse Optimization through Online Learning

Andreas Bärmann, Sebastian Pokutta, Oskar Schneider

End-to-End Differentiable Adversarial Imitation Learning

Nir Baram, Oron Anschel, Itai Caspi, Shie Mannor

Dynamic Word Embeddings

Robert Bamler, Stephan Mandt

Lost Relatives of the Gumbel Trick

Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller

Spectral Learning from a Single Trajectory under Finite-State Policies

Borja Balle, Odalric-ambrym Maillard

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

David Balduzzi, Brian McWilliams, Tony Butler-Yeoman

The Shattered Gradients Problem: If resnets are the answer, then what is the question?

David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma

Strongly-Typed Agents are Guaranteed to Interact Safely

David Balduzzi