ICML 2017

434 papers

Differentially Private Clustering in High-Dimensional Euclidean Spaces

Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang

Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms

Arturs Backurs, Christos Tzamos

Learning Algorithms for Active Learning

Philip Bachman, Alessandro Sordoni, Adam Trischler

Distributed and Provably Good Seedings for k-Means in Constant Rounds

Olivier Bachem, Mario Lucic, Andreas Krause

Uniform Deviation Bounds for k-Means Clustering

Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

Learning the Structure of Generative Models without Labeled Data

Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Re

Minimax Regret Bounds for Reinforcement Learning

Mohammad Gheshlaghi azar, Ian Osband, Rémi Munos

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker

An Alternative Softmax Operator for Reinforcement Learning

Kavosh Asadi, Michael L. Littman

A Closer Look at Memorization in Deep Networks

Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang

Wasserstein Generative Adversarial Networks

Martin Arjovsky, Soumith Chintala, Léon Bottou

Oracle Complexity of Second-Order Methods for Finite-Sum Problems

Yossi Arjevani, Ohad Shamir

Deep Voice: Real-time Neural Text-to-Speech

Sercan Ö. Arık, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky

A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency

Ron Appel, Pietro Perona

Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

Oron Anschel, Nir Baram, Nahum Shimkin

Modular Multitask Reinforcement Learning with Policy Sketches

Jacob Andreas, Dan Klein, Sergey Levine

An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation

David Anderson, Ming Gu

Input Convex Neural Networks

Brandon Amos, Lei Xu, J. Zico Kolter

OptNet: Differentiable Optimization as a Layer in Neural Networks

Brandon Amos, J. Zico Kolter

Near-Optimal Design of Experiments via Regret Minimization

Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU

Zeyuan Allen-Zhu, Yuanzhi Li

Faster Principal Component Regression and Stable Matrix Chebyshev Approximation

Zeyuan Allen-Zhu, Yuanzhi Li

Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition

Zeyuan Allen-Zhu, Yuanzhi Li

Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter

Zeyuan Allen-Zhu

Learning Continuous Semantic Representations of Symbolic Expressions

Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton

A Semismooth Newton Method for Fast, Generic Convex Programming

Alnur Ali, Eric Wong, J. Zico Kolter

Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis

Ahmed M. Alaa, Scott Hu, Mihaela Schaar

Connected Subgraph Detection with Mirror Descent on SDPs

Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama

Local Bayesian Optimization of Motor Skills

Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann

The Price of Differential Privacy for Online Learning

Naman Agarwal, Karan Singh

Constrained Policy Optimization

Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel

A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions

Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh

Uncovering Causality from Multivariate Hawkes Integrated Cumulants

Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas, Iacopo Mastromatteo, Jean-François Muzy