ICML 2015

270 papers

\ell_1,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods Zirui Zhou, Qi Zhang, Anthony Man-Cho So

A Bayesian Nonparametric Procedure for Comparing Algorithms Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon

A Convex Exemplar-Based Approach to MAD-Bayes Dirichlet Process Mixture Models En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon

A Convex Optimization Framework for Bi-Clustering Shiau Hong Lim, Yudong Chen, Huan Xu

A Deeper Look at Planning as Learning from Replay Harm Vanseijen, Rich Sutton

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-Reduced Data Yining Wang, Yu-Xiang Wang, Aarti Singh

A Divide and Conquer Framework for Distributed Graph Clustering Wenzhuo Yang, Huan Xu

A Fast Variational Approach for Learning Markov Random Field Language Models Yacine Jernite, Alexander Rush, David Sontag

A General Analysis of the Convergence of ADMM Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan

A Hybrid Approach for Probabilistic Inference Using Random Projections Michael Zhu, Stefano Ermon

A Linear Dynamical System Model for Text David Belanger, Sham Kakade

A Low Variance Consistent Test of Relative Dependency Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko

A Lower Bound for the Optimization of Finite Sums Alekh Agarwal, Leon Bottou

A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis Pinghua Gong, Jieping Ye

A Multitask Point Process Predictive Model Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin

A Nearly-Linear Time Framework for Graph-Structured Sparsity Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

A New Generalized Error Path Algorithm for Model Selection Bin Gu, Charles Ling

A Probabilistic Model for Dirty Multi-Task Feature Selection Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning Debarghya Ghoshdastidar, Ambedkar Dukkipati

A Relative Exponential Weighing Algorithm for Adversarial Utility-Based Dueling Bandits Pratik Gajane, Tanguy Urvoy, Fabrice Clérot

A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate Ohad Shamir

A Theoretical Analysis of Metric Hypothesis Transfer Learning Michaël Perrot, Amaury Habrard

A Trust-Region Method for Stochastic Variational Inference with Applications to Streaming Data Lucas Theis, Matt Hoffman

A Unified Framework for Outlier-Robust PCA-like Algorithms Wenzhuo Yang, Huan Xu

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Abstraction Selection in Model-Based Reinforcement Learning Nan Jiang, Alex Kulesza, Satinder Singh

Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams Rose Yu, Dehua Cheng, Yan Liu

Active Nearest Neighbors in Changing Environments Christopher Berlind, Ruth Urner

Adaptive Belief Propagation Georgios Papachristoudis, John Fisher

Adaptive Stochastic Alternating Direction Method of Multipliers Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li

Adding vs. Averaging in Distributed Primal-Dual Optimization Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac

Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction Sébastien Giguère, Amélie Rolland, Francois Laviolette, Mario Marchand

Alpha-Beta Divergences Discover Micro and Macro Structures in Data Karthik Narayan, Ali Punjani, Pieter Abbeel

An Aligned Subtree Kernel for Weighted Graphs Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization Necdet Aybat, Zi Wang, Garud Iyengar

An Embarrassingly Simple Approach to Zero-Shot Learning Bernardino Romera-Paredes, Philip Torr

An Empirical Exploration of Recurrent Network Architectures Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process Amar Shah, David Knowles, Zoubin Ghahramani

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

An Online Learning Algorithm for Bilinear Models Yuanbin Wu, Shiliang Sun

Approval Voting and Incentives in Crowdsourcing Nihar Shah, Dengyong Zhou, Yuval Peres

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin

Asymmetric Transfer Learning with Deep Gaussian Processes Melih Kandemir

Atomic Spatial Processes Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté

Attribute Efficient Linear Regression with Distribution-Dependent Sampling Doron Kukliansky, Ohad Shamir

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy

Bayesian and Empirical Bayesian Forests Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle

Bayesian Multiple Target Localization Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak

BilBOWA: Fast Bilingual Distributed Representations Without Word Alignments Stephan Gouws, Yoshua Bengio, Greg Corrado

Bimodal Modelling of Source Code and Natural Language Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei

Binary Embedding: Fundamental Limits and Fast Algorithm Xinyang Yi, Constantine Caramanis, Eric Price

Bipartite Edge Prediction via Transductive Learning over Product Graphs Hanxiao Liu, Yiming Yang

Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization Tyler Johnson, Carlos Guestrin

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions Taehoon Lee, Sungroh Yoon

Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi

Cascading Bandits: Learning to Rank in the Cascade Model Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing

Celeste: Variational Inference for a Generative Model of Astronomical Images Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Mr Prabhat

Cheap Bandits Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Remi Munos

Classification with Low Rank and Missing Data Elad Hazan, Roi Livni, Yishay Mansour

Community Detection Using Time-Dependent Personalized PageRank Haim Avron, Lior Horesh

Complete Dictionary Recovery Using Nonconvex Optimization Ju Sun, Qing Qu, John Wright

Complex Event Detection Using Semantic Saliency and Nearly-Isotonic SVM Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu

Compressing Neural Networks with the Hashing Trick Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen

Consistent Estimation of Dynamic and Multi-Layer Block Models Qiuyi Han, Kevin Xu, Edoardo Airoldi

Consistent Multiclass Algorithms for Complex Performance Measures Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal

Context-Based Unsupervised Data Fusion for Decision Making Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela Schaar

Controversy in Mechanistic Modelling with Gaussian Processes Benn Macdonald, Catherine Higham, Dirk Husmeier

Convergence Rate of Bayesian Tensor Estimator and Its Minimax Optimality Taiji Suzuki

Convex Calibrated Surrogates for Hierarchical Classification Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal

Convex Formulation for Learning from Positive and Unlabeled Data Marthinus Du Plessis, Gang Niu, Masashi Sugiyama

Convex Learning of Multiple Tasks and Their Structure Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco

Coordinate Descent Converges Faster with the Gauss-Southwell Rule than Random Selection Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke

Coresets for Nonparametric Estimation - The Case of DP-Means Olivier Bachem, Mario Lucic, Andreas Krause

Correlation Clustering in Data Streams KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback Adith Swaminathan, Thorsten Joachims

CUR Algorithm for Partially Observed Matrices Miao Xu, Rong Jin, Zhi-Hua Zhou

Dealing with Small Data: On the Generalization of Context Trees Ralf Eggeling, Mikko Koivisto, Ivo Grosse

Deep Edge-Aware Filters Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia

Deep Learning with Limited Numerical Precision Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan

Deep Unsupervised Learning Using Nonequilibrium Thermodynamics Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli

Deterministic Independent Component Analysis Ruitong Huang, Andras Gyorgy, Csaba Szepesvári

Differentially Private Bayesian Optimization Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger

DiSCO: Distributed Optimization for Self-Concordant Empirical Loss Yuchen Zhang, Xiao Lin

Discovering Temporal Causal Relations from Subsampled Data Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger

Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM Ching-Pei Lee, Dan Roth

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds Yuchen Zhang, Martin Wainwright, Michael Jordan

Distributed Gaussian Processes Marc Deisenroth, Jun Wei Ng

Distributed Inference for Dirichlet Process Mixture Models Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani

Distributional Rank Aggregation, and an Axiomatic Analysis Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar

Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung

DP-Space: Bayesian Nonparametric Subspace Clustering with Small-Variance Asymptotics Yining Wang, Jun Zhu

DRAW: A Recurrent Neural Network for Image Generation Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra

Dynamic Sensing: Better Classification Under Acquisition Constraints Oran Richman, Shie Mannor

Efficient Learning in Large-Scale Combinatorial Semi-Bandits Zheng Wen, Branislav Kveton, Azin Ashkan

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele

Enabling Scalable Stochastic Gradient-Based Inference for Gaussian Processes by Employing the Unbiased LInear System SolvEr (ULISSE) Maurizio Filippone, Raphael Engler

Entropic Graph-Based Posterior Regularization Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble

Entropy Evaluation Based on Confidence Intervals of Frequency Estimates : Application to the Learning of Decision Trees Mathieu Serrurier, Henri Prade

Entropy-Based Concentration Inequalities for Dependent Variables Liva Ralaivola, Massih-Reza Amini

Exponential Integration for Hamiltonian Monte Carlo Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha

Fast Kronecker Inference in Gaussian Processes with Non-Gaussian Likelihoods Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola

Faster Cover Trees Mike Izbicki, Christian Shelton

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets Dan Garber, Elad Hazan

Feature-Budgeted Random Forest Feng Nan, Joseph Wang, Venkatesh Saligrama

Fictitious Self-Play in Extensive-Form Games Johannes Heinrich, Marc Lanctot, David Silver

Finding Galaxies in the Shadows of Quasars with Gaussian Processes Roman Garnett, Shirley Ho, Jeff Schneider

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis Zhuang Ma, Yichao Lu, Dean Foster

Fixed-Point Algorithms for Learning Determinantal Point Processes Zelda Mariet, Suvrit Sra

Following the Perturbed Leader for Online Structured Learning Alon Cohen, Tamir Hazan

From Word Embeddings to Document Distances Matt Kusner, Yu Sun, Nicholas Kolkin, Kilian Weinberger

Functional Subspace Clustering with Application to Time Series Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu

Gated Feedback Recurrent Neural Networks Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio

Generalization Error Bounds for Learning to Rank: Does the Length of Document Lists Matter? Ambuj Tewari, Sougata Chaudhuri

Generative Moment Matching Networks Yujia Li, Kevin Swersky, Rich Zemel

Geometric Conditions for Subspace-Sparse Recovery Chong You, Rene Vidal

Global Convergence of Stochastic Gradient Descent for Some Non-Convex Matrix Problems Christopher De Sa, Christopher Re, Kunle Olukotun

Gradient-Based Hyperparameter Optimization Through Reversible Learning Dougal Maclaurin, David Duvenaud, Ryan Adams

Guaranteed Tensor Decomposition: A Moment Approach Gongguo Tang, Parikshit Shah

Harmonic Exponential Families on Manifolds Taco Cohen, Max Welling

Hashing for Distributed Data Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu

Hidden Markov Anomaly Detection Nico Goernitz, Mikio Braun, Marius Kloft

High Confidence Policy Improvement Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh

High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? Senjian An, Farid Boussaid, Mohammed Bennamoun

How Hard Is Inference for Structured Prediction? Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning K. Lakshmanan, Ronald Ortner, Daniil Ryabko

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Yarin Gal, Richard Turner

Inference in a Partially Observed Queuing Model with Applications in Ecology Kevin Winner, Garrett Bernstein, Dan Sheldon

Inferring Graphs from Cascades: A Sparse Recovery Framework Jean Pouget-Abadie, Thibaut Horel

Information Geometry and Minimum Description Length Networks Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet

Intersecting Faces: Non-Negative Matrix Factorization with New Guarantees Rong Ge, James Zou

Is Feature Selection Secure Against Training Data Poisoning? Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

K-Hyperplane Hinge-Minimax Classifier Margarita Osadchy, Tamir Hazan, Daniel Keren

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) Andrew Wilson, Hannes Nickisch

Landmarking Manifolds with Gaussian Processes Dawen Liang, John Paisley

Large-Scale Distributed Dependent Nonparametric Trees Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing

Large-Scale Log-Determinant Computation Through Stochastic Chebyshev Expansions Insu Han, Dmitry Malioutov, Jinwoo Shin

Large-Scale Markov Decision Problems with KL Control Cost and Its Application to Crowdsourcing Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data Yarin Gal, Yutian Chen, Zoubin Ghahramani

Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models James Foulds, Shachi Kumar, Lise Getoor

Learning Deep Structured Models Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun

Learning Fast-Mixing Models for Structured Prediction Jacob Steinhardt, Percy Liang

Learning from Corrupted Binary Labels via Class-Probability Estimation Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson

Learning Local Invariant Mahalanobis Distances Ethan Fetaya, Shimon Ullman

Learning Parametric-Output HMMs with Two Aliased States Roi Weiss, Boaz Nadler

Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas

Learning Scale-Free Networks by Dynamic Node Specific Degree Prior Qingming Tang, Siqi Sun, Jinbo Xu

Learning Submodular Losses with the Lovasz Hinge Jiaqian Yu, Matthew Blaschko

Learning to Search Better than Your Teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé, John Langford

Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan

Learning Word Representations with Hierarchical Sparse Coding Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith

Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen

Long Short-Term Memory over Recursive Structures Xiaodan Zhu, Parinaz Sobihani, Hongyu Guo

Low Rank Approximation Using Error Correcting Coding Matrices Shashanka Ubaru, Arya Mazumdar, Yousef Saad

Low-Rank Matrix Recovery from Row-and-Column Affine Measurements Or Zuk, Avishai Wagner

MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

Manifold-Valued Dirichlet Processes Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap Tim Salimans, Diederik Kingma, Max Welling

Markov Mixed Membership Models Aonan Zhang, John Paisley

Message Passing for Collective Graphical Models Tao Sun, Dan Sheldon, Akshat Kumar

Metadata Dependent Mondrian Processes Yi Wang, Bin Li, Yang Wang, Fang Chen

Mind the Duality Gap: Safer Rules for the Lasso Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Modeling Order in Neural Word Embeddings at Scale Andrew Trask, David Gilmore, Matthew Russell

Moderated and Drifting Linear Dynamical Systems Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler, Kobus Barnard

MRA-Based Statistical Learning from Incomplete Rankings Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz

Multi-Instance Multi-Label Learning in the Presence of Novel Class Instances Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga

Multi-Task Learning for Subspace Segmentation Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell

Multi-View Sparse Co-Clustering via Proximal Alternating Linearized Minimization Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi

Multiview Triplet Embedding: Learning Attributes in Multiple Maps Ehsan Amid, Antti Ukkonen

Nested Sequential Monte Carlo Methods Christian Naesseth, Fredrik Lindsten, Thomas Schon

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin

Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu

Non-Stationary Approximate Modified Policy Iteration Boris Lesner, Bruno Scherrer

Off-Policy Model-Based Learning Under Unknown Factored Dynamics Assaf Hallak, Francois Schnitzler, Timothy Mann, Shie Mannor

On Deep Multi-View Representation Learning Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes

On Greedy Maximization of Entropy Dravyansh Sharma, Ashish Kapoor, Amit Deshpande

On Identifying Good Options Under Combinatorially Structured Feedback in Finite Noisy Environments Yifan Wu, Andras Gyorgy, Csaba Szepesvari

On Symmetric and Asymmetric LSHs for Inner Product Search Behnam Neyshabur, Nathan Srebro

On TD(0) with Function Approximation: Concentration Bounds and a Centered Variant with Exponential Convergence Nathaniel Korda, Prashanth La

On the Optimality of Multi-Label Classification Under Subset Zero-One Loss for Distributions Satisfying the Composition Property Maxime Gasse, Alexandre Aussem, Haytham Elghazel

On the Rate of Convergence and Error Bounds for LSTD(λ) Manel Tagorti, Bruno Scherrer

On the Relationship Between Sum-Product Networks and Bayesian Networks Han Zhao, Mazen Melibari, Pascal Poupart

Online Learning of Eigenvectors Dan Garber, Elad Hazan, Tengyu Ma

Online Time Series Prediction with Missing Data Oren Anava, Elad Hazan, Assaf Zeevi

Online Tracking by Learning Discriminative Saliency mAP with Convolutional Neural Network Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

Optimal and Adaptive Algorithms for Online Boosting Alina Beygelzimer, Satyen Kale, Haipeng Luo

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-Armed Bandit Problem with Multiple Plays Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Optimizing Neural Networks with Kronecker-Factored Approximate Curvature James Martens, Roger Grosse

Optimizing Non-Decomposable Performance Measures: A Tale of Two Classes Harikrishna Narasimhan, Purushottam Kar, Prateek Jain

Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya

Ordinal Mixed Membership Models Seppo Virtanen, Mark Girolami

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor

PASSCoDe: Parallel ASynchronous Stochastic Dual Co-Ordinate Descent Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

PeakSeg: Constrained Optimal Segmentation and Supervised Penalty Learning for Peak Detection in Count Data Toby Hocking, Guillem Rigaill, Guillaume Bourque

Phrase-Based Image Captioning Remi Lebret, Pedro Pinheiro, Ronan Collobert

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani

Preference Completion: Large-Scale Collaborative Ranking from Pairwise Comparisons Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu-Xiang Wang, Stephen Fienberg, Alex Smola

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks Jose Miguel Hernandez-Lobato, Ryan Adams

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach Jason Pacheco, Erik Sudderth

PU Learning for Matrix Completion Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA Bo Xin, David Wipf

Qualitative Multi-Armed Bandits: A Quantile-Based Approach Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier

Rademacher Observations, Private Data, and Boosting Richard Nock, Giorgio Patrini, Arik Friedman

Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions Alina Ene, Huy Nguyen

Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal

Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki

Reified Context Models Jacob Steinhardt, Percy Liang

Removing Systematic Errors for Exoplanet Search via Latent Causes Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

Risk and Regret of Hierarchical Bayesian Learners Jonathan Huggins, Josh Tenenbaum

Robust Estimation of Transition Matrices in High Dimensional Heavy-Tailed Vector Autoregressive Processes Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo

Robust Partially Observable Markov Decision Process Takayuki Osogami

Safe Exploration for Optimization with Gaussian Processes Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret Haitham Bou Ammar, Rasul Tutunov, Eric Eaton

Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices Jie Wang, Jieping Ye

Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems Qiang Zhou, Qi Zhao

Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams

Scalable Deep Poisson Factor Analysis for Topic Modeling Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin

Scalable Model Selection for Large-Scale Factorial Relational Models Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes Yves-Laurent Kom Samo, Stephen Roberts

Scalable Variational Inference in Log-Supermodular Models Josip Djolonga, Andreas Krause

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix Roger Grosse, Ruslan Salakhudinov

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio

Simple Regret for Infinitely Many Armed Bandits Alexandra Carpentier, Michal Valko

Sparse Subspace Clustering with Missing Entries Congyuan Yang, Daniel Robinson, Rene Vidal

Sparse Variational Inference for Generalized GP Models Rishit Sheth, Yuyang Wang, Roni Khardon

Spectral Clustering via the Power Method - Provably Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons Yuxin Chen, Changho Suh

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares Garvesh Raskutti, Michael Mahoney

Stay on Path: PCA Along Graph Paths Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran

Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba, Zheng Qu, Peter Richtarik

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization Peilin Zhao, Tong Zhang

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang, Xiao Lin

Streaming Sparse Principal Component Analysis Wenzhuo Yang, Huan Xu

Strongly Adaptive Online Learning Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Structural Maxent Models Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

Submodularity in Data Subset Selection and Active Learning Kai Wei, Rishabh Iyer, Jeff Bilmes

Subsampling Methods for Persistent Homology Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry Wasserman

Support Matrix Machines Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li

Surrogate Functions for Maximizing Precision at the Top Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

Swept Approximate Message Passing for Sparse Estimation Andre Manoel, Florent Krzakala, Eric Tramel, Lenka Zdeborovà

Telling Cause from Effect in Deterministic Linear Dynamical Systems Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve

The Benefits of Learning with Strongly Convex Approximate Inference Ben London, Bert Huang, Lise Getoor

The Composition Theorem for Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling Michael Betancourt

The Hedge Algorithm on a Continuum Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen

The Kendall and Mallows Kernels for Permutations Yunlong Jiao, Jean-Philippe Vert

The Ladder: A Reliable Leaderboard for Machine Learning Competitions Avrim Blum, Moritz Hardt

The Power of Randomization: Distributed Submodular Maximization on Massive Datasets Rafael Barbosa, Alina Ene, Huy Nguyen, Justin Ward

Theory of Dual-Sparse Regularized Randomized Reduction Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

Threshold Influence Model for Allocating Advertising Budgets Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura

Towards a Learning Theory of Cause-Effect Inference David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin

Towards a Lower Sample Complexity for Robust One-Bit Compressed Sensing Rongda Zhu, Quanquan Gu

Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains Katharina Blechschmidt, Joachim Giesen, Soeren Laue

Training Deep Convolutional Neural Networks to Play Go Christopher Clark, Amos Storkey

Trust Region Policy Optimization John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz

Un-Regularizing: Approximate Proximal Point and Faster Stochastic Algorithms for Empirical Risk Minimization Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford

Universal Value Function Approximators Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

Unsupervised Domain Adaptation by Backpropagation Yaroslav Ganin, Victor Lempitsky

Unsupervised Learning of Video Representations Using LSTMs Nitish Srivastava, Elman Mansimov, Ruslan Salakhudinov

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations Tam Le, Marco Cuturi

Variational Generative Stochastic Networks with Collaborative Shaping Philip Bachman, Doina Precup

Variational Inference for Gaussian Process Modulated Poisson Processes Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts

Variational Inference with Normalizing Flows Danilo Rezende, Shakir Mohamed

Vector-Space Markov Random Fields via Exponential Families Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar

Weight Uncertainty in Neural Network Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra

Yinyang K-Means: A Drop-in Replacement of the Classic K-Means with Consistent Speedup Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz