ICML 2016

322 papers

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit Dhillon

Noisy Activation Functions

Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio

Slice Sampling on Hamiltonian Trajectories

Benjamin Bloem-Reddy, John Cunningham

Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, Frank Wood

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Yunseong Hwang, Anh Tong, Jaesik Choi

Learning Representations for Counterfactual Inference

Fredrik Johansson, Uri Shalit, David Sontag

Correcting Forecasts with Multifactor Neural Attention

Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull

Stochastic Discrete Clenshaw-Curtis Quadrature

Nico Piatkowski, Katharina Morik

Group Equivariant Convolutional Networks

Taco Cohen, Max Welling

Horizontally Scalable Submodular Maximization

Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause

Controlling the distance to a Kemeny consensus without computing it

Yunlong Jiao, Anna Korba, Eric Sibony

Model-Free Trajectory Optimization for Reinforcement Learning

Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko

Shifting Regret, Mirror Descent, and Matrices

András György, Csaba Szepesvári

PHOG: Probabilistic Model for Code

Pavol Bielik, Veselin Raychev, Martin Vechev

Power of Ordered Hypothesis Testing

Lihua Lei, William Fithian

Near Optimal Behavior via Approximate State Abstraction

David Abel, David Hershkowitz, Michael Littman

Learning Mixtures of Plackett-Luce Models

Zhibing Zhao, Peter Piech, Lirong Xia

Partition Functions from Rao-Blackwellized Tempered Sampling

David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

Energetic Natural Gradient Descent

Philip Thomas, Bruno Castro Silva, Christoph Dann, Emma Brunskill

Fast Algorithms for Segmented Regression

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Epigraph projections for fast general convex programming

Po-Wei Wang, Matt Wytock, Zico Kolter

Provable Algorithms for Inference in Topic Models

Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra

Fixed Point Quantization of Deep Convolutional Networks

Darryl Lin, Sachin Talathi, Sreekanth Annapureddy

Domain Adaptation with Conditional Transferable Components

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour

Continuous Deep Q-Learning with Model-based Acceleration

Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

Tensor Decomposition via Joint Matrix Schur Decomposition

Nicolo Colombo, Nikos Vlassis

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach

The Label Complexity of Mixed-Initiative Classifier Training

Jina Suh, Xiaojin Zhu, Saleema Amershi

Control of Memory, Active Perception, and Action in Minecraft

Junhyuk Oh, Valliappa Chockalingam, Satinder, Honglak Lee

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications

Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

ADIOS: Architectures Deep In Output Space

Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh Gupta, Stefano Ermon

Algorithms for Optimizing the Ratio of Submodular Functions

Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford

Clustering High Dimensional Categorical Data via Topographical Features

Chao Chen, Novi Quadrianto

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel

Robust Random Cut Forest Based Anomaly Detection on Streams

Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers

Discriminative Embeddings of Latent Variable Models for Structured Data

Hanjun Dai, Bo Dai, Le Song

Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning

Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics

Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy

Gromov-Wasserstein Averaging of Kernel and Distance Matrices

Gabriel Peyré, Marco Cuturi, Justin Solomon

Computationally Efficient Nyström Approximation using Fast Transforms

Si Si, Cho-jui Hsieh, Inderjit Dhillon

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Quanming Yao, James Kwok

A Theory of Generative ConvNet

Jianwen Xie, Yang Lu, Song-chun Zhu, Yingnian Wu

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco

Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent

A Kernel Test of Goodness of Fit

Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums

Anton Rodomanov, Dmitry Kropotov

Learning and Inference via Maximum Inner Product Search

Stephen Mussmann, Stefano Ermon

Speeding up k-means by approximating Euclidean distances via block vectors

Thomas Bottesch, Thomas Bühler, Markus Kächele

Estimation from Indirect Supervision with Linear Moments

Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang

Pricing a Low-regret Seller

Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod

Dynamic Capacity Networks

Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle

Greedy Column Subset Selection: New Bounds and Distributed Algorithms

Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh

Preconditioning Kernel Matrices

Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Jacob Abernethy, Elad Hazan

Bidirectional Helmholtz Machines

Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio

Recycling Randomness with Structure for Sublinear time Kernel Expansions

Krzysztof Choromanski, Vikas Sindhwani

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

Yutian Chen, Zoubin Ghahramani

Conditional Bernoulli Mixtures for Multi-label Classification

Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang

Geometric Mean Metric Learning

Pourya Zadeh, Reshad Hosseini, Suvrit Sra

A Box-Constrained Approach for Hard Permutation Problems

Cong Han Lim, Steve Wright

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

David Inouye, Pradeep Ravikumar, Inderjit Dhillon

Discrete Distribution Estimation under Local Privacy

Peter Kairouz, Keith Bonawitz, Daniel Ramage

Expressiveness of Rectifier Networks

Xingyuan Pan, Vivek Srikumar

Learning Population-Level Diffusions with Generative RNNs

Tatsunori Hashimoto, David Gifford, Tommi Jaakkola

Estimating Cosmological Parameters from the Dark Matter Distribution

Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho

Dynamic Memory Networks for Visual and Textual Question Answering

Caiming Xiong, Stephen Merity, Richard Socher

Evasion and Hardening of Tree Ensemble Classifiers

Alex Kantchelian, J. D. Tygar, Anthony Joseph

Generalization and Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Zheng Wen

Deconstructing the Ladder Network Architecture

Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio

Recovery guarantee of weighted low-rank approximation via alternating minimization

Yuanzhi Li, Yingyu Liang, Andrej Risteski

Principal Component Projection Without Principal Component Analysis

Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford

Generalization Properties and Implicit Regularization for Multiple Passes SGM

Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco

Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model

Xinze Guan, Raviv Raich, Weng-Keen Wong

Stratified Sampling Meets Machine Learning

Edo Liberty, Kevin Lang, Konstantin Shmakov

Early and Reliable Event Detection Using Proximity Space Representation

Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy

Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation

Huan Gui, Jiawei Han, Quanquan Gu

Robust Principal Component Analysis with Side Information

Kai-Yang Chiang, Cho-jui Hsieh, Inderjit Dhillon

Generalized Direct Change Estimation in Ising Model Structure

Farideh Fazayeli, Arindam Banerjee

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery

Ian En-Hsu Yen, Xin lin, Jiong Zhang, Pradeep Ravikumar, Inderjit Dhillon

On the Power and Limits of Distance-Based Learning

Periklis Papakonstantinou, Jia Xu, Guang Yang

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Tudor Achim, Ashish Sabharwal, Stefano Ermon

Markov-modulated Marked Poisson Processes for Check-in Data

Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand

Cross-Graph Learning of Multi-Relational Associations

Hanxiao Liu, Yiming Yang

Isotonic Hawkes Processes

Yichen Wang, Bo Xie, Nan Du, Le Song

Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer

Hierarchical Decision Making In Electricity Grid Management

Gal Dalal, Elad Gilboa, Shie Mannor

Variational Inference for Monte Carlo Objectives

Andriy Mnih, Danilo Rezende

Sequence to Sequence Training of CTC-RNNs with Partial Windowing

Kyuyeon Hwang, Wonyong Sung

Training Deep Neural Networks via Direct Loss Minimization

Yang Song, Alexander Schwing, Richard, Raquel Urtasun

Efficient Algorithms for Adversarial Contextual Learning

Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire

Discrete Deep Feature Extraction: A Theory and New Architectures

Thomas Wiatowski, Michael Tschannen, Aleksandar Stanić, Philipp Grohs, Helmut Boelcskei

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip Thomas, Emma Brunskill

Differentially Private Policy Evaluation

Borja Balle, Maziar Gomrokchi, Doina Precup

Pliable Rejection Sampling

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard