ICML 2015

270 papers

A New Generalized Error Path Algorithm for Model Selection

Bin Gu, Charles Ling

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property

Maxime Gasse, Alexandre Aussem, Haytham Elghazel

Deterministic Independent Component Analysis

Ruitong Huang, András György, Csaba Szepesvári

Inference in a Partially Observed Queuing Model with Applications in Ecology

Kevin Winner, Garrett Bernstein, Dan Sheldon

A trust-region method for stochastic variational inference with applications to streaming data

Lucas Theis, Matt Hoffman

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

Yu-Xiang Wang, Stephen Fienberg, Alex Smola

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions

Taehoon Lee, Sungroh Yoon

Moderated and Drifting Linear Dynamical Systems

Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler

Sparse Subspace Clustering with Missing Entries

Congyuan Yang, Daniel Robinson, René Vidal

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization

Necdet Aybat, Zi Wang, Garud Iyengar

PU Learning for Matrix Completion

Cho-jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon

Entropy-Based Concentration Inequalities for Dependent Variables

Liva Ralaivola, Massih-Reza Amini

Multi-instance multi-label learning in the presence of novel class instances

Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models

En-Hsu Yen, Xin lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

James Martens, Roger Grosse

Consistent Multiclass Algorithms for Complex Performance Measures

Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal

Fixed-point algorithms for learning determinantal point processes

Zelda Mariet, Suvrit Sra

High Confidence Policy Improvement

Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh

PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent

Cho-jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

Haitham Bou Ammar, Rasul Tutunov, Eric Eaton

Complete Dictionary Recovery Using Nonconvex Optimization

Ju Sun, Qing Qu, John Wright

An Empirical Exploration of Recurrent Network Architectures

Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever

Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems

Christopher De Sa, Christopher Re, Kunle Olukotun

Optimal and Adaptive Algorithms for Online Boosting

Alina Beygelzimer, Satyen Kale, Haipeng Luo

A Deeper Look at Planning as Learning from Replay

Harm Vanseijen, Rich Sutton

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix

Roger Grosse, Ruslan Salakhudinov

Intersecting Faces: Non-negative Matrix Factorization With New Guarantees

Rong Ge, James Zou

Compressing Neural Networks with the Hashing Trick

Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen

Distributed Inference for Dirichlet Process Mixture Models

Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani

Modeling Order in Neural Word Embeddings at Scale

Andrew Trask, David Gilmore, Matthew Russell

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Jascha Sohl-dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli

Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

Qingming Tang, Siqi Sun, Jinbo Xu

Correlation Clustering in Data Streams

KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

Yves-Laurent Kom Samo, Stephen Roberts

Removing systematic errors for exoplanet search via latent causes

Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing

A Fast Variational Approach for Learning Markov Random Field Language Models

Yacine Jernite, Alexander Rush, David Sontag

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach

Jason Pacheco, Erik Sudderth

Online Time Series Prediction with Missing Data

Oren Anava, Elad Hazan, Assaf Zeevi

How Hard is Inference for Structured Prediction?

Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

Scalable Bayesian Optimization Using Deep Neural Networks

Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish

Binary Embedding: Fundamental Limits and Fast Algorithm

Xinyang Yi, Constantine Caramanis, Eric Price

An embarrassingly simple approach to zero-shot learning

Bernardino Romera-Paredes, Philip Torr

Subsampling Methods for Persistent Homology

Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo

Cheap Bandits

Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos

Bimodal Modelling of Source Code and Natural Language

Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei

Gradient-based Hyperparameter Optimization through Reversible Learning

Dougal Maclaurin, David Duvenaud, Ryan Adams

Distributional Rank Aggregation, and an Axiomatic Analysis

Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar

Celeste: Variational inference for a generative model of astronomical images

Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman

Phrase-based Image Captioning

Rémi Lebret, Pedro Pinheiro, Ronan Collobert

Context-based Unsupervised Data Fusion for Decision Making

Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela Schaar

Gated Feedback Recurrent Neural Networks

Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio

Learning to Search Better than Your Teacher

Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume III, John Langford

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville

A Hybrid Approach for Probabilistic Inference using Random Projections

Michael Zhu, Stefano Ermon

A Multitask Point Process Predictive Model

Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin

Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction

Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand

Low-Rank Matrix Recovery from Row-and-Column Affine Measurements

Or Zuk, Avishai Wagner

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations

Tam Le, Marco Cuturi

Entropic Graph-based Posterior Regularization

Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble

Feature-Budgeted Random Forest

Feng Nan, Joseph Wang, Venkatesh Saligrama

Adding vs. Averaging in Distributed Primal-Dual Optimization

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik

Variational Generative Stochastic Networks with Collaborative Shaping

Philip Bachman, Doina Precup

Submodularity in Data Subset Selection and Active Learning

Kai Wei, Rishabh Iyer, Jeff Bilmes

Bayesian Multiple Target Localization

Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak

The Kendall and Mallows Kernels for Permutations

Yunlong Jiao, Jean-philippe Vert

On Symmetric and Asymmetric LSHs for Inner Product Search

Behnam Neyshabur, Nathan Srebro

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon

Discovering Temporal Causal Relations from Subsampled Data

Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger

Trust Region Policy Optimization

John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz

Bipartite Edge Prediction via Transductive Learning over Product Graphs

Hanxiao Liu, Yiming Yang

Active Nearest Neighbors in Changing Environments

Christopher Berlind, Ruth Urner

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

José Miguel Hernández-Lobato, Ryan Adams

Convex Calibrated Surrogates for Hierarchical Classification

Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes

Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo

Hidden Markov Anomaly Detection

Nico Goernitz, Mikio Braun, Marius Kloft

Scalable Deep Poisson Factor Analysis for Topic Modeling

Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin

Variational Inference for Gaussian Process Modulated Poisson Processes

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts

Scalable Variational Inference in Log-supermodular Models

Josip Djolonga, Andreas Krause

Community Detection Using Time-Dependent Personalized PageRank

Haim Avron, Lior Horesh

Learning Deep Structured Models

Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

Andrew Wilson, Hannes Nickisch

Training Deep Convolutional Neural Networks to Play Go

Christopher Clark, Amos Storkey

Harmonic Exponential Families on Manifolds

Taco Cohen, Max Welling

Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices

Jie Wang, Jieping Ye

Deep Learning with Limited Numerical Precision

Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan

Stay on path: PCA along graph paths

Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran

Generative Moment Matching Networks

Yujia Li, Kevin Swersky, Rich Zemel

A Theoretical Analysis of Metric Hypothesis Transfer Learning

Michaël Perrot, Amaury Habrard

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

José Miguel Hernández-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani

Is Feature Selection Secure against Training Data Poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert

A Convex Optimization Framework for Bi-Clustering

Shiau Hong Lim, Yudong Chen, Huan Xu

Deep Edge-Aware Filters

Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia

Qualitative Multi-Armed Bandits: A Quantile-Based Approach

Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier

Large-scale Distributed Dependent Nonparametric Trees

Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing

Hashing for Distributed Data

Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu

Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection

Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke

Learning Submodular Losses with the Lovasz Hinge

Jiaqian Yu, Matthew Blaschko

Weight Uncertainty in Neural Network

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra