ICML Beijing

Cycle I Accepted Papers

Cycle I proceedings are available at http://jmlr.org/proceedings/papers/v32/.

A Discriminative Latent Variable Model for Online Clustering: Rajhans Samdani, Kai-Wei Chang, Dan Roth

Kernel Mean Estimation and Stein Effect: Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf

Demystifying Information-Theoretic Clustering: Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo

Covering Number for Efficient Heuristic-based POMDP Planning: Zongzhang Zhang, David Hsu, Wee Sun Lee

The Coherent Loss Function for Classification: Wenzhuo Yang, Melvyn Sim, Huan Xu

Fast Stochastic Alternating Direction Method of Multipliers: Wenliang Zhong, James Kwok

Active Detection via Adaptive Submodularity: Yuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge Wich, Andreas Krause

Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization: Shai Shalev-Shwartz, Tong Zhang

An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization: Qihang Lin, Lin Xiao

Recurrent Convolutional Neural Networks for Scene Labeling: Pedro Pinheiro, Ronan Collobert

A Statistical Perspective on Algorithmic Leveraging: Ping Ma, Michael Mahoney, Bin Yu

Thompson Sampling for Complex Online Problems: Aditya Gopalan, Shie Mannor, Yishay Mansour

Boosting multi-step autoregressive forecasts: Souhaib Ben Taieb, Rob Hyndman

A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data: Arun Rajkumar, Shivani Agarwal

Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations: Timothy Mann, Shie Mannor

Latent Bandits: Odalric-Ambrym Maillard, Shie Mannor

Fast Allocation of Gaussian Process Experts: Trung Nguyen, Edwin Bonilla

Von Mises-Fisher Clustering Models: Siddharth Gopal, Yiming Yang

Convergence rates for persistence diagram estimation in Topological Data Analysis: Frédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel

Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs: Fabian Gieseke, Justin Heinermann, Cosmin Oancea, Christian Igel

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget: Anoop Korattikara, Yutian Chen, Max Welling

Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis: Jian Tang, Zhaoshi Meng, Xuanlong Nguyen, Qiaozhu Mei, Ming Zhang

The Inverse Regression Topic Model: Maxim Rabinovich, David Blei

A Consistent Histogram Estimator for Exchangeable Graph Models: Stanley Chan, Edoardo Airoldi

Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data: Benjamin Letham, Wei Sun, Anshul Sheopuri

Towards Minimax Online Learning with Unknown Time Horizon: Haipeng Luo, Robert Schapire

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball: Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry

Margins, Kernels and Non-linear Smoothed Perceptrons: Aaditya Ramdas, Javier Peña

Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models: Shike Mei, Jun Zhu, Jerry Zhu

Learning Theory and Algorithms for revenue optimization in second price auctions with reserve: Mehryar Mohri, Andres Munoz Medina

Low-density Parity Constraints for Hashing-Based Discrete Integration: Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations: Yevgeny Seldin, Peter Bartlett, Koby Crammer, Yasin Abbasi-Yadkori

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts: Tien Vu Nguyen, Dinh Phung, Xuanlong Nguyen, Swetha Venkatesh, Hung Bui

Large-Margin Metric Learning for Constrained Partitioning Problems: Rémi Lajugie, Francis Bach, Sylvain Arlot

Wasserstein Propagation for Semi-Supervised Learning: Justin Solomon, Raif Rustamov, Guibas Leonidas, Adrian Butscher

Max-Margin Infinite Hidden Markov Models: Aonan Zhang, Jun Zhu, Bo Zhang

Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function: Yong Liu, Shali Jiang, Shizhong Liao

Generalized Exponential Concentration Inequality for Renyi Divergence Estimation: Shashank Singh, Barnabas Poczos

Boosting with Online Binary Learners for the Multiclass Bandit Problem: Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu

Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm: Tasuku Soma, Naonori Kakimura, Kazuhiro Inaba, Ken-ichi Kawarabayashi

Computing Parametric Ranking Models via Rank-Breaking: Hossein Azari Soufiani, David Parkes, Lirong Xia

Tracking Adversarial Targets: Yasin Abbasi-Yadkori, Peter Bartlett, Varun Kanade

Online Bayesian Passive-Aggressive Learning: Tianlin Shi, Jun Zhu

Deterministic Policy Gradient Algorithms: David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller

Modeling Correlated Arrival Events with Latent Semi-Markov Processes: Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach: Rémi Bardenet, Arnaud Doucet, Chris Holmes

Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost: Ferdinando Cicalese, Eduardo Laber, Aline Medeiros Saettler

Condensed Filter Tree for Cost-Sensitive Multi-Label Classification: Chun-Liang Li, Hsuan-Tien Lin

On Measure Concentration of Random Maximum A-Posteriori Perturbations: Francesco Orabona, Tamir Hazan, Anand Sarwate, Tommi Jaakkola

Bias in Natural Actor-Critic Algorithms: Philip Thomas

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning: François Denis, Mattias Gybels, Amaury Habrard

On Modelling Non-linear Topical Dependencies: Zhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang

A Deep and Tractable Density Estimator: Benigno Uria, Iain Murray, Hugo Larochelle

(Near) Dimension Independent Risk Bounds for Differentially Private Learning: Prateek Jain, Abhradeep Guha Thakurta

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels: Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael Mahoney

Discriminative Features via Generalized Eigenvectors: Nikos Karampatziakis, Paul Mineiro

Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint: Ji Liu, Jieping Ye, Ryohei Fujimaki

Online Learning in Markov Decision Processes with Changing Cost Sequences: Travis Dick, Andras Gyorgy, Csaba Szepesvari

Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms: Richard Combes, Alexandre Proutiere

Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection: Arun Iyer, Saketha Nath, Sunita Sarawagi

Asymptotically consistent estimation of the number of change points in highly dependent time series: Azadeh Khaleghi, Daniil Ryabko

Coordinate-descent for learning orthogonal matrices through Givens rotations: Uri Shalit, Gal Chechik

Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search: Anshumali Shrivastava, Ping Li

A Divide-and-Conquer Solver for Kernel Support Vector Machines: Cho-Jui Hsieh, Si Si, Inderjit Dhillon

Nuclear Norm Minimization via Active Subspace Selection: Cho-Jui Hsieh, Peder Olsen

Provable Bounds for Learning Some Deep Representations: Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma

Large-scale Multi-label Learning with Missing Labels: Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon

Learning Graphs with a Few Hubs: Rashish Tandon, Pradeep Ravikumar

Agnostic Bayesian Learning of Ensembles: Alexandre Lacoste, Mario Marchand, Franois Laviolette, Hugo Larochelle

Towards an optimal stochastic alternating direction method of multipliers: Samaneh Azadi, Suvrit Sra

Spherical Hamiltonian Monte Carlo for Constrained Target Distributions: Shiwei Lan, Bo Zhou, Babak Shahbaba

Efficient Continuous-Time Markov Chain Estimation: Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition: Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell

Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers: Dani Yogatama, Noah Smith

Narrowing the Gap: Random Forests In Theory and In Practice: Misha Denil, David Matheson, Nando De Freitas

Coherent Matrix Completion: Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward

Admixture of Poisson MRFs: A Topic Model with Word Dependencies: David Inouye, Pradeep Ravikumar, Inderjit Dhillon

True Online TD(lambda): Harm van Seijen, Rich Sutton

Memory Efficient Kernel Approximation: Si Si, Cho-Jui Hsieh, Inderjit Dhillon

Learning Sum-Product Networks with Direct and Indirect Variable Interactions: Amirmohammad Rooshenas, Daniel Lowd

Hamiltonian Monte Carlo Without Detailed Balance: Jascha Sohl-Dickstein, Mayur Mudigonda, Michael DeWeese

Filtering with Abstract Particles: Jacob Steinhardt, Percy Liang

Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers: Taiji Suzuki

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction: Jian Zhou, Olga Troyanskaya

An Efficient Approach for Assessing Hyperparameter Importance: Frank Hutter, Holger Hoos, Kevin Leyton-Brown

Cycle II Accepted Papers

Global Graph Kernels Using Geometric Embeddings: Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi, Chiranjib Bhattacharyya

Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data: Zhiyuan Chen, Bing Liu

K-means Recovers ICA Filters when Independent Components are Sparse: Alon Vinnikov, Shai Shalev-Shwartz

Learning Mixtures of Linear Classifiers: Yuekai Sun, Stratis Ioannidis, Andrea Montanari

The Falling Factorial Basis and Its Statistical Applications: Yu-Xiang Wang, Ryan Tibshirani, Alex Smola

Nonmyopic $\epsilon$-Bayes-Optimal Active Learning of Gaussian Processes: Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli

A Unifying View of Representer Theorems: Andreas Argyriou, Francesco Dinuzzo

Online Clustering of Bandits: Claudio Gentile, Shuai Li, Giovanni Zappella

Cold-start Active Learning with Robust Ordinal Matrix Factorization: Neil Houlsby, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

Multivariate Maximal Correlation Analysis: Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm

Efficient Label Propagation: Yasuhiro Fujiwara, Go Irie

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm: Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schoelkopf

Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising: Ling Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han

Putting MRFs on a Tensor Train: Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov

Efficient Algorithms for Robust One-bit Compressive Sensing: Lijun Zhang, Jinfeng Yi, Rong Jin

Learning Complex Neural Network Policies with Trajectory Optimization: Sergey Levine, Vladlen Koltun

Composite Quantization for Approximate Nearest Neighbor Search: Ting Zhang, Chao Du, Jingdong Wang

Local Ordinal Embedding: Yoshikazu Terada, Ulrike von Luxburg

Reducing Dueling Bandits to Cardinal Bandits: Nir Ailon, Zohar Karnin, Thorsten Joachims

Large-margin Weakly Supervised Dimensionality Reduction: Chang Xu, Dacheng Tao, Chao Xu, Yong Rui

Joint Inference of Multiple Label Types in Large Networks: Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus Macskassy

Hard-margin Active Linear Regression: Zohar Karnin, Elad Hazan

Maximum Margin Multiclass Nearest Neighbors: Aryeh Kontorovich, Roi Weiss

Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications: Tian Lin, Bruno Abrahao, Robert Kleinberg, John Lui, Wei Chen

Sparse Meta-Gaussian Information Bottleneck: Melani Rey, Volker Roth, Thomas Fuchs

Nonparametric Estimation of Renyi Divergence and Friends: Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

Robust Inverse Covariance Estimation under Noisy Measurements: Jun-Kun Wang, Ting-Wei Lin, Shou-de Lin

Bayesian Optimization with Inequality Constraints: Jacob Gardner, Matt Kusner, Kilian Weinberger, John Cunningham, Zhixiang (Eddie) Xu

Circulant Binary Embedding: Felix Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang

Multiple Testing under Dependence via Semiparametric Graphical Models: Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

Making Fisher Discriminant Analysis Scalable: Bojun Tu, Hui Qian, Zhihua Zhang

Hierarchical Dirichlet Scaling Process: Dongwoo Kim, Alice Oh

Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process: Issei Sato, Hiroshi Nakagawa

A PAC-Bayesian Bound for Lifelong Learning: Anastasia Pentina, Christoph Lampert

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method: Ohad Shamir, Nati Srebro, Tong Zhang

Concept Drift Detection Through Resampling: Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer

Anti-differentiating Approximation Algorithms: A case study with Min-cuts, Spectral, and Flow: David Gleich, Michael Mahoney

A Bayesian Wilcoxon Signed-rank Test Based on the Dirichlet Process: Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon, Fabrizio Ruggeri

Min-Max Problems on Factor Graphs: Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner

Distributed Stochastic Gradient MCMC: Sungjin Ahn, Babak Shahbaba, Max Welling

Nearest Neighbors Using Compact Sparse Codes: Anoop Cherian

Optimal Mean Robust Principal Component Analysis: Feiping Nie, Jianjun Yuan, Heng Huang

Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows: Robert Busa-Fekete, Balázs Szörényi, Eyke Huellermeier

Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations: Bilal Ahmed, Karen Blackmon, Thomas Thesen, Ruben Kuzniecky, Chad Carlson, Jacqueline French, Werner Doyle, Carla Brodley

A Physics-Based Model Prior for Object-Oriented MDPs: Jonathan Scholz, Martin Levihn, Charles Isbell

Outlier Path: A Homotopy Algorithm for Robust SVM: Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi

Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data: Naiyan Wang, Dit-Yan Yeung

Latent Confusion Analysis by Normalized Gamma Construction: Issei Sato, Kashima Hisashi, Hiroshi Nakagawa

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems: Aaron Defazio, Justin Domke, Tiberio Caetano

Ensemble Methods for Structured Prediction: Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri

Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance: Simone Romano, James Bailey, Vinh Nguyen, Karin Verspoor

Preserving Modes and Messages via Diverse Particle Selection: Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth

Nonlinear Information-Theoretic Compressive Measurement Design: Liming Wang, Abolfazl Razi, Miguel Rodrigues, Robert Calderbank, Lawrence Carin

Dual Query: Practical Private Query Release for High Dimensional Data: Marco Gaboardi Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

Deep Boosting: Corinna Cortes, Mehryar Mohri, Umar Syed

Distributed Representations of Sentences and Documents: Quoc Le, Tomas Mikolov

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models: Robert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay Pande

Online Multi-Task Learning for Policy Gradient Methods: Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, Matthew Taylor

Affinity Weighted Embedding: Jason Weston, Ron Weiss, Hector Yee

Learning the Parameters of Determinantal Point Process Kernels: Raja Hafiz Affandi, Emily Fox, Ryan Adams, Ben Taskar

Discrete Chebyshev Classifiers: Elad Eban, Elad Mezuman, Amir Globerson

Deep AutoRegressive Networks: Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

A Convergence Rate Analysis for LogitBoost, MART and Their Variant: Peng Sun, Tong Zhang, Jie Zhou

Inferning with High Girth Graphical Models: Uri Heinemann, Amir Globerson

Learning Latent Variable Gaussian Graphical Models: Zhaoshi Meng, Brian Eriksson, Al Hero

Stochastic Backpropagation and Approximate Inference in Deep Generative Models: Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

One Practical Algorithm for Both Stochastic and Adversarial Bandits: Yevgeny Seldin, Aleksandrs Slivkins

Robust and Efficient Kernel Hyperparameter Paths with Guarantees: Joachim Giesen, Soeren Laue, Patrick Wieschollek

Active Transfer Learning under Model Shift: Xuezhi Wang, Tzu-Kuo Huang, Jeff Schneider

Approximate Policy Iteration Schemes: A Comparison: Bruno Scherrer

Robust and Efficient Representation Learning with Nonnegativity Constraints: Tsung-Han Lin

Sample Efficient Reinforcement Learning with Gaussian Processes: Robert Grande, Thomas Walsh, Jonathan How

Memory and Computation Efficient PCA via Very Sparse Random Projections: Farhad Pourkamali Anaraki, Shannon Hughes

Time-Regularized Interrupting Options (TRIO): Timothy Mann, Daniel Mankowitz, Shie Mannor

Randomized Nonlinear Component Analysis: David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schoelkopf

High Order Regularization for Semi-Supervised Learning of Structured Output Problems: Yujia Li, Rich Zemel

Transductive Learning with Multi-class Volume Approximation: Gang Niu, Bo Dai, Christoffel du Plessis, Masashi Sugiyama

Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison: Borja Balle, William Hamilton, Joelle Pineau

Effective Bayesian Modeling of Groups of Related Count Time Series: Nicolas Chapados

Variational Inference for Sequential Distance Dependent Chinese Restaurant Process: Sergey Bartunov, Dmitry Vetrov

Discovering Latent Network Structure in Point Process Data: Scott Linderman, Ryan Adams

A Kernel Independence Test for Random Processes: Kacper Chwialkowski, Arthur Gretton

Learning Representations for Interacting Manifolds with Higher-order Boltzmann Machines: Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee

Learning Modular Structures from Network Data and Node Variables: Elham Azizi, Edoardo Airoldi

Probabilistic Partial Canonical Correlation Analysis: Yusuke Mukuta, Tatsuya Harada

Skip Context Tree Switching: Marc Bellemare, Joel Veness, Erik Talvitie, Alex Graves

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians: Christopher Tosh, Sanjoy Dasgupta

Marginalized Denoising Auto-encoders for Nonlinear Representations: Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations: David Barber, Yali Wang

Fast Multi-stage Submodular Maximization: Kai Wei, Rishabh Iyer, Jeff Bilmes

Programming by Feedback: Marc Schoenauer, Riad Akrour, Michele Sebag, Jean-Christophe Souplet

Probabilistic Matrix Factorization with Non-random Missing Data: Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

Pursuit-Evasion Without Regrets, with an Application to Trading: Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka

The f-Adjusted Laplacian: a Diagonal Perturbation with a Geometric Interpretation: Sven Kurras, Ulrike von Luxburg, Gilles Blanchard

Riemannian Pursuit for Big Matrix Recovery: Mingkui Tan, Ivor W. Tsang, Li Wang, Jialin Pan, Bart Vandereycken

Dynamic Programming Boosting for Discriminative Macro-Action Discovery: Leonidas Lefakis, Francois Fleuret

Resource-Efficient Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback: Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill

Weighted Graph Clustering with Non-Uniform Uncertainties: Yudong Chen, Shiau Hong Lim, Huan Xu

GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results: Philip Thomas

A Bayesian Framework for Online Classifier Ensemble: Qinxun Bai, Henry Lam, Stan Sclaroff

Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm: Jacob Steinhardt, Percy Liang

Gaussian Approximation of Collective Graphical Models: Liping Liu, Daniel Sheldon, Thomas Dietterich

One-Bit Object Detection: On Learning to Localize Objects with Minimal Supervision: Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell

Multiresolution Matrix Factorization: Risi Kondor, Nedelina Teneva, Vikas Garg

Learnability of the Superset Label Learning Problem: Liping Liu, Thomas Dietterich

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits: Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire

Structured Recurrent Temporal Restricted Boltzmann Machines: Roni Mittelman, Benjamin Kuipers, Silvio Savarese, Honglak Lee

Scalable and Robust Bayesian Inference via the Median Posterior: Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David Dunson

Kernel Adaptive Metropolis-Hastings: Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

Input Warping for Bayesian Optimization of Non-stationary Functions: Jasper Snoek, Kevin Swersky, Rich Zemel, Ryan Adams

Stochastic Gradient Hamiltonian Monte Carlo: Tianqi Chen, Emily Fox, Carlos Guestrin

A Deep Semi-NMF Model for Learning Hidden Representations: George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern Schuller

Asynchronous Distributed ADMM Algorithm for Global Variable Consensus Optimization: Ruiliang Zhang, James Kwok

Spectral Regularization for Max-Margin Sequence Tagging: Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson

Learning by Stretching Deep Networks: Gaurav Pandey, Ambedkar Dukkipati

Nonnegative Sparse PCA with Provable Guarantees: Megasthenis Asteris, Alexandros Dimakis, Dimitris Papailiopoulos

Active Learning of Parameterized Skills: Bruno Da Silva, George Konidaris, Andrew Barto

Learning Ordered Representations with Nested Dropout: Oren Rippel, Michael Gelbart, Ryan Adams

Learning the Irreducible Representations of Commutative Lie Groups: Taco Cohen, Max Welling

Towards End-To-End Speech Recognition with Recurrent Neural Networks: Alex Graves, Navdeep Jaitly

Multi-period Trading Prediction Markets with Connections to Machine Learning: Jinli Hu, Amos Storkey

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets: Diederik Kingma, Max Welling

Neural Variational Inference and Learning in Belief Networks: Andriy Mnih, Karol Gregor

Scalable Nonparametric Bayesian Analysis of Incomplete Multiway Data: Piyush Rai, Yingjian Wang, Lawrence Carin

Beta Diffusion Trees: Creighton Heaukulani, David Knowles, Zoubin Ghahramani

Learning Character-level Representations for Part-of-Speech Tagging: Cicero Dos Santos, Bianca Zadrozny

Saddle Points and Accelerated Perceptron Algorithms: Adams Wei Yu, fatma Kilinc-Karzan, Jaime Carbonell

Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization: Hua Wang, Feiping Nie, Heng Huang

Learning from Contagion (Without Timestamps): Kareem Amin, Hoda Heidari, Michael Kearns

Stochastic Variational Inference for Bayesian Time Series Models: Matthew Johnson, Alan Willsky

A Clockwork RNN: Jan Koutnik, Klaus Greff, Faustino Gomez, Juergen Schmidhuber

Estimating Latent-Variable Graphical Models using Moments and Likelihoods: Arun Tejasvi Chaganty, Percy Liang

Universal Matrix Completion: Srinadh Bhojanapalli, Prateek Jain

Finding Dense Subgraphs via Low-Rank Bilinear Optimization: Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis

Compositional Morphology for Word Representations and Language Modelling: Jan Botha, Phil Blunsom

Learning Polynomials with Neural Networks: Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang

Exponential Family Matrix Completion under Structural Constraints: Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh

Sample-based Approximate Regularization: Philip Bachman, Amir-Massoud Farahmand, Doina Precup

A Compilation Target for Probabilistic Programming Languages: Brooks Paige, Frank Wood

Adaptive Monte-Carlo via Bandit Allocation: James Neufeld, Andras Gyorgy, Csaba Szepesvari, Dale Schuurmans

Efficient Dimensionality Reduction for High-Dimensional Network Estimation: Safiye Celik, Benjamin Logsdon, Su-In Lee

Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes: E. Busra Celikkaya, Christian Shelton

Doubly Stochastic Variational Bayes for non-Conjugate Inference: Michalis Titsias, Miguel Lázaro-Gredilla

Efficient Learning of Mahalanobis Metrics for Ranking: Daryl Lim, Gert Lanckriet

GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare: Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal

A Reversible Infinite HMM using Normalised Random Measures: Konstantina Palla, David Knowles, Zoubin Ghahramani

Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing: Benjamin Haeffele, Rene Vidal, Eric Young

Influence Function Learning in Information Diffusion Networks: Nan Du, Yingyu Liang, Le Song, Maria Balcan

An Information Geometry of Statistical Manifold Learning: Ke Sun, Stéphane Marchand-Maillet

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem: Masrour Zoghi, Shimon Whiteson, Remi Munos, Maarten de Rijke

Compact Random Feature Maps: Raffay Hamid, Alex Gittens, Ying Xiao, Dennis Decoste

Concentration in Unbounded Metric Spaces and Algorithmic Stability: Aryeh Kontorovich

Heavy-tailed Regression with a Generalized Median-of-means: Daniel Hsu, Sivan Sabato

Spectral Bandits for Smooth Graph Functions: Remi Munos, Michal Valko, Branislav Kveton, Tomas Kocak

Robust Principal Component Analysis with Complex Noise: Qian Zhao, Deyu Meng, Lei Zhang, Wangmeng Zuo, Zongben Xu

Scalable Semidefinite Relaxation for Maximum A Posteriori Estimation: Qixing Huang, Yuxin Chen, Guibas Leonidas

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery: Cun Mu, Bo Huang, John Wright, Donald Goldfarb

Automated Inference of Point of View from User Interactions in Collective Intelligence Venues: Sanmay Das, Allen Lavoie

Orthogonal Rank-One Matrix Pursuit for Matrix Completion: Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye

Near-Optimal Joint Object Matching via Convex Relaxation: Yuxin Chen, Guibas Leonidas, Qixing Huang

Convex Total Least Squares: Dmitry Malioutov, Nikolai Slavov

On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection: Pratik Jawanpuria, Manik Varma, Saketha Nath

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization: Xiaotong Yuan, Ping Li, Tong Zhang

Learning With Priors: Jean Honorio, Tommi Jaakkola

Geodesic Distance Function Learning via Heat Flows on Vector Fields: Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

Active Teaching for Crowdsourcing Classification: Adish Singla, Ilija Bogunovic, Gabor Bartok, Amin Karbasi, Andreas Krause

On the Convergence of No-regret Learning in Selfish Routing: Benjamin Drighès, Walid Krichene, Alexandre Bayen

Offline Evaluation of Recommendation Systems: Olivier Nicol, Jérémie Mary, Philippe Preux

Scaling Up Robust MDPs by Reinforcement Learning: Aviv Tamar, Huan Xu, Shie Mannor

Marginal Structured SVM with Hidden Variables: Wei Ping, Qiang Liu, Alex Ihler

From Exponential to Linear Complexity When Learning Practical Markov Random Fields: Yariv Mizrahi, Nando De Freitas, Luis Tenorio

Pitfalls in the Use of Parallel Inference for the Dirichlet Process: Yarin Gal, Zoubin Ghahramani

Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing: Yuan Zhou, Xi Chen, Jian Li

Deep Generative Stochastic Networks Trainable by Backprop: Yoshua Bengio, Eric Laufer, Jason Yosinski

A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models: Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Jieping Ye

Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting: Yudong Chen, Jiaming Xu

Gaussian Process Optimization with Mutual Information: Emile Contal, Nicolas Vayatis

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy: Dengyong Zhou, Qiang Liu, John Platt, Christopher Meek

Exchangeable Variable Models: Mathias Niepert, Pedro Domingos

Clustering in the Presence of Background Noise: Nika Haghtalab, Shai Ben-David

Safe Screening with Variational Inequalities and Its Application to Lasso: Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye

Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks: Shan-Hung Wu, Hao-Heng Chien, Kuan-Hua Lin, Philip Yu

Signal Recovery from $\ell_p$ Pooling Representations: Joan Bruna Estrach, Arthur Szlam, Yann LeCun

PAC-inspired Option Discovery in Lifelong Reinforcement Learning: Emma Brunskill, Lihong Li

Multi-label Classification via Feature-aware Implicit Label Space Encoding: Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications: Sebastien Bratieres, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani

Anomaly Ranking as Supervised Bipartite Ranking: Stephan Clémençon, Sylvain Robbiano

Hierarchical Quasi-Clustering Methods for Asymmetric Networks: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra

Rectangular Tiling Process: Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda

Two-Stage Metric Learning: Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices: Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

Elementary Estimators for High-Dimensional Linear Regression: Eunho Yang, Aurelie Lozano, Pradeep Ravikumar

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments: Eunho Yang, Aurelie Lozano, Pradeep Ravikumar

Learning with Smoothness: Pointwise, Graph-based, Probabilistic: Yuan Fang, Kevin Chang, Hady Lauw

Bayesian Max-margin Multi-Task Learning with Data Augmentation: Chengtao Li, Jun Zhu, Jianfei Chen

Sparse Reinforcement Learning via Convex Optimization: Zhiwei Qin, Weichang Li

Gaussian Process Classification and Active Learning with Multiple Annotators: Filipe Rodrigues, Francisco Pereira, Bernardete Ribeiro

Structured Prediction of Network Response: Hongyu Su, Aristides Gionis, Juho Rousu

An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy: Gavin Taylor, Connor Geer, David Piekut

Optimization Equivalence of Divergences Improves Neighbor Embedding: Zhirong Yang, Jaakko Peltonen, Samuel Kaski

An Asynchronous Parallel Stochastic Coordinate Descent Algorithm: Ji Liu, Steve Wright, Christopher Re, Srikrishna Sridhar, Vicotr Bittorf

Consistency of Causal Inference under the Additive Noise Model: Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schoelkopf

Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm: Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun

Linear Programming for Large-Scale Markov Decision Problems: Alan Malek, Yasin Abbasi-Yadkori, Peter Bartlett

Linear Time Solver for Primal SVM: Feiping Nie, Yizhen Huang, Heng Huang

Implicit Particle Sequential Monte Carlo: Seong-Hwan Jun, Alexandre Bouchard-Côté

Scaling SVM and Least Absolute Deviations via Exact Data Reduction: Jie Wang, Jieping Ye

Latent Semantic Representation Learning for Scene Classification: Xin Li, Yuhong Guo

Least Squares Revisited: Scalable Approaches for Multi-class Prediction: Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant

Local Algorithms for Interactive Clustering: Pranjal Awasthi, Konstantin Voevodski, Maria Balcan

Learning and Planning with Relational Uncertainty Predicates over the Existence of Objects: Vien Ngo, Marc Toussaint

A New Q(\lambda): Rich Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt

On Robustness and Regularization of Structural Support Vector Machines: Mohamad Ali Torkamani, Daniel Lowd

Guess-Averse Loss Functions for Cost-Sensitive Multiclass Boosting: Oscar Beijbom, Mohammad Saberian, Nuno Vasconcelos, David Kriegman

Multimodal Neural Language Models: Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel

An Adaptive Low Dimensional quasi-Newton Sum of Functions Optimizer: Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli

Alternating Minimization for Mixed Linear Regression: Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

Stochastic Neighbor Compression: Matt Kusner, Stephen Tyree, Kilian Weinberger, Kunal Agrawal

Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification: Junfeng Wen, Chun-Nam Yu, Russell Greiner

Nonparametric Estimation of Multi-View Latent Variable Models: Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

Structured Generative Models of Natural Source Code: Chris Maddison, Daniel Tarlow

A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers for High-dimensional Data: Jinfeng Yi, Lijun Zhang, Jun Wang, Rong Jin, Anil Jain

Stochastic Approximation with Implicit Updates. Applications in Robust Online Learning of GLMs: Panagiotis Toulis, Edoardo Airoldi, Jason Rennie

Coding for Random Projections: Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

Fast Computation of Wasserstein Barycenters: Marco Cuturi, Arnaud Doucet

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