ICML 2016

322 papers

Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan

How to Fake Multiply by a Gaussian Matrix

Michael Kapralov, Vamsi Potluru, David Woodruff

A Convolutional Attention Network for Extreme Summarization of Source Code

Miltiadis Allamanis, Hao Peng, Charles Sutton

Interactive Bayesian Hierarchical Clustering

Sharad Vikram, Sanjoy Dasgupta

Complex Embeddings for Simple Link Prediction

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard

Fast DPP Sampling for Nystrom with Application to Kernel Methods

Chengtao Li, Stefanie Jegelka, Suvrit Sra

Mixture Proportion Estimation via Kernel Embeddings of Distributions

Harish Ramaswamy, Clayton Scott, Ambuj Tewari

The Arrow of Time in Multivariate Time Series

Stefan Bauer, Bernhard Schölkopf, Jonas Peters

Recurrent Orthogonal Networks and Long-Memory Tasks

Mikael Henaff, arthur szlam, Yann LeCun

Persistent RNNs: Stashing Recurrent Weights On-Chip

Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates

Learning Convolutional Neural Networks for Graphs

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu

Dueling Network Architectures for Deep Reinforcement Learning

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot

Associative Long Short-Term Memory

Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Alexander Rakhlin, Karthik Sridharan

Nonparametric Canonical Correlation Analysis

Tomer Michaeli, Weiran Wang, Karen Livescu

Barron and Cover’s Theory in Supervised Learning and its Application to Lasso

Masanori Kawakita, Jun’ichi Takeuchi

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

A Simple and Strongly-Local Flow-Based Method for Cut Improvement

Nate Veldt, David Gleich, Michael Mahoney

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap

Pareto Frontier Learning with Expensive Correlated Objectives

Amar Shah, Zoubin Ghahramani

The Sum-Product Theorem: A Foundation for Learning Tractable Models

Abram Friesen, Pedro Domingos

Graying the black box: Understanding DQNs

Tom Zahavy, Nir Ben-Zrihem, Shie Mannor

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu

Differential Geometric Regularization for Supervised Learning of Classifiers

Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff

Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert Gower, Donald Goldfarb, Peter Richtarik

Softened Approximate Policy Iteration for Markov Games

Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin

The knockoff filter for FDR control in group-sparse and multitask regression

Ran Dai, Rina Barber

Meta-Learning with Memory-Augmented Neural Networks

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap

On Graduated Optimization for Stochastic Non-Convex Problems

Elad Hazan, Kfir Yehuda Levy, Shai Shalev-shwartz

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq

No penalty no tears: Least squares in high-dimensional linear models

Xiangyu Wang, David Dunson, Chenlei Leng

Opponent Modeling in Deep Reinforcement Learning

He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daume III

Hierarchical Compound Poisson Factorization

Mehmet Basbug, Barbara Engelhardt

Stochastic Optimization for Multiview Representation Learning using Partial Least Squares

Raman Arora, Poorya Mianjy, Teodor Marinov

Train and Test Tightness of LP Relaxations in Structured Prediction

Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag

Gaussian quadrature for matrix inverse forms with applications

Chengtao Li, Suvrit Sra, Stefanie Jegelka

Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well

Ozgur Simsek, Simón Algorta, Amit Kothiyal

Pixel Recurrent Neural Networks

Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu

Dictionary Learning for Massive Matrix Factorization

Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux

Neural Variational Inference for Text Processing

Yishu Miao, Lei Yu, Phil Blunsom

Learning Granger Causality for Hawkes Processes

Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

Christos Louizos, Max Welling

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching

Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier

An optimal algorithm for the Thresholding Bandit Problem

Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier

ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission

Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela Schaar

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization

Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li

Extended and Unscented Kitchen Sinks

Edwin Bonilla, Daniel Steinberg, Alistair Reid

No-Regret Algorithms for Heavy-Tailed Linear Bandits

Andres Munoz Medina, Scott Yang

Gaussian process nonparametric tensor estimator and its minimax optimality

Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

Black-box Optimization with a Politician

Sébastien Bubeck, Yin Tat Lee

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

Andre Martins, Ramón Astudillo

On collapsed representation of hierarchical Completely Random Measures

Gaurav Pandey, Ambedkar Dukkipati

Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design

William Hoiles, Mihaela Schaar

Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne, Vianney Perchet

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Christopher De Sa, Chris Re, Kunle Olukotun

Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra

A ranking approach to global optimization

Cedric Malherbe, Emile Contal, Nicolas Vayatis

Optimal Classification with Multivariate Losses

Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon

One-Shot Generalization in Deep Generative Models

Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra

Black-Box Alpha Divergence Minimization

Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernández-lobato

Rich Component Analysis

Rong Ge, James Zou

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernández-lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang Bui, Daniel Hernández-lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner

Starting Small - Learning with Adaptive Sample Sizes

Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann

Importance Sampling Tree for Large-scale Empirical Expectation

Olivier Canevet, Cijo Jose, Francois Fleuret

Auxiliary Deep Generative Models

Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther

Extreme F-measure Maximization using Sparse Probability Estimates

Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx

Non-negative Matrix Factorization under Heavy Noise

Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani

Estimating Accuracy from Unlabeled Data: A Bayesian Approach

Emmanouil Antonios Platanios, Avinava Dubey, Tom Mitchell

Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control

Prashanth L.A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvári

Solving Ridge Regression using Sketched Preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-shwartz

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury

On the Statistical Limits of Convex Relaxations

Zhaoran Wang, Quanquan Gu, Han Liu

Fast Constrained Submodular Maximization: Personalized Data Summarization

Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Dmitry Ulyanov, Vadim Lebedev, Andrea, Victor Lempitsky

K-Means Clustering with Distributed Dimensions

Hu Ding, Yu Liu, Lingxiao Huang, Jian Li

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search

Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone

Collapsed Variational Inference for Sum-Product Networks

Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos

Distributed Clustering of Linear Bandits in Peer to Peer Networks

Nathan Korda, Balázs Szörényi, Shuai Li

Strongly-Typed Recurrent Neural Networks

David Balduzzi, Muhammad Ghifary

False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking

Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao

Factored Temporal Sigmoid Belief Networks for Sequence Learning

Jiaming Song, Zhe Gan, Lawrence Carin

Variance-Reduced and Projection-Free Stochastic Optimization

Elad Hazan, Haipeng Luo

Conservative Bandits

Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári

Contextual Combinatorial Cascading Bandits

Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Train faster, generalize better: Stability of stochastic gradient descent

Moritz Hardt, Ben Recht, Yoram Singer

DCM Bandits: Learning to Rank with Multiple Clicks

Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen

A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling

Mostafa Rahmani, Geroge Atia

Learning to Filter with Predictive State Inference Machines

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

Learning End-to-end Video Classification with Rank-Pooling

Basura Fernando, Stephen Gould

Learning to Generate with Memory

Chongxuan Li, Jun Zhu, Bo Zhang

Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks

Devansh Arpit, Yingbo Zhou, Bhargava Kota, Venu Govindaraju