Volume 37: International Conference on Machine Learning, 7-9 July 2015, Lille, France


Editors: Francis Bach, David Blei


Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Peilin Zhao, Tong Zhang ; PMLR 37:1-9

Approval Voting and Incentives in Crowdsourcing

Nihar Shah, Dengyong Zhou, Yuval Peres ; PMLR 37:10-19

A low variance consistent test of relative dependency

Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko ; PMLR 37:20-29

An Aligned Subtree Kernel for Weighted Graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock ; PMLR 37:30-39

Spectral Clustering via the Power Method - Provably

Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens ; PMLR 37:40-48

Information Geometry and Minimum Description Length Networks

Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet ; PMLR 37:49-58

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation

Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele ; PMLR 37:59-68

Adaptive Stochastic Alternating Direction Method of Multipliers

Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li ; PMLR 37:69-77

A Lower Bound for the Optimization of Finite Sums

Alekh Agarwal, Leon Bottou ; PMLR 37:78-86

Learning Word Representations with Hierarchical Sparse Coding

Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith ; PMLR 37:87-96

Learning Transferable Features with Deep Adaptation Networks

Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan ; PMLR 37:97-105

Robust partially observable Markov decision process

Takayuki Osogami ; PMLR 37:106-115

On the Relationship between Sum-Product Networks and Bayesian Networks

Han Zhao, Mazen Melibari, Pascal Poupart ; PMLR 37:116-124

Learning from Corrupted Binary Labels via Class-Probability Estimation

Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson ; PMLR 37:125-134

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu ; PMLR 37:135-143

A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate

Ohad Shamir ; PMLR 37:144-152

Attribute Efficient Linear Regression with Distribution-Dependent Sampling

Doron Kukliansky, Ohad Shamir ; PMLR 37:153-161

Learning Local Invariant Mahalanobis Distances

Ethan Fetaya, Shimon Ullman ; PMLR 37:162-168

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

Zhuang Ma, Yichao Lu, Dean Foster ; PMLR 37:169-178

Abstraction Selection in Model-based Reinforcement Learning

Nan Jiang, Alex Kulesza, Satinder Singh ; PMLR 37:179-188

Surrogate Functions for Maximizing Precision at the Top

Purushottam Kar, Harikrishna Narasimhan, Prateek Jain ; PMLR 37:189-198

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes

Harikrishna Narasimhan, Purushottam Kar, Prateek Jain ; PMLR 37:199-208

Coresets for Nonparametric Estimation - the Case of DP-Means

Olivier Bachem, Mario Lucic, Andreas Krause ; PMLR 37:209-217

A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits

Pratik Gajane, Tanguy Urvoy, Fabrice Clérot ; PMLR 37:218-227

Functional Subspace Clustering with Application to Time Series

Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu ; PMLR 37:228-237

Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams

Rose Yu, Dehua Cheng, Yan Liu ; PMLR 37:238-247

Atomic Spatial Processes

Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté ; PMLR 37:248-256

Classification with Low Rank and Missing Data

Elad Hazan, Roi Livni, Yishay Mansour ; PMLR 37:257-266

Dynamic Sensing: Better Classification under Acquisition Constraints

Oran Richman, Shie Mannor ; PMLR 37:267-275

A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis

Pinghua Gong, Jieping Ye ; PMLR 37:276-284

Telling cause from effect in deterministic linear dynamical systems

Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve ; PMLR 37:285-294

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos ; PMLR 37:295-304

Theory of Dual-sparse Regularized Randomized Reduction

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu ; PMLR 37:305-314

Generalization error bounds for learning to rank: Does the length of document lists matter?

Ambuj Tewari, Sougata Chaudhuri ; PMLR 37:315-323

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Toby Hocking, Guillem Rigaill, Guillaume Bourque ; PMLR 37:324-332

Mind the duality gap: safer rules for the Lasso

Olivier Fercoq, Alexandre Gramfort, Joseph Salmon ; PMLR 37:333-342

A General Analysis of the Convergence of ADMM

Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan ; PMLR 37:343-352

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Yuchen Zhang, Xiao Lin ; PMLR 37:353-361

DiSCO: Distributed Optimization for Self-Concordant Empirical Loss

Yuchen Zhang, Xiao Lin ; PMLR 37:362-370

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons

Yuxin Chen, Changho Suh ; PMLR 37:371-380

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs

Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor ; PMLR 37:381-390

Structural Maxent Models

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed ; PMLR 37:391-399

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning

Debarghya Ghoshdastidar, Ambedkar Dukkipati ; PMLR 37:400-409

The Benefits of Learning with Strongly Convex Approximate Inference

Ben London, Bert Huang, Lise Getoor ; PMLR 37:410-418

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA

Bo Xin, David Wipf ; PMLR 37:419-427

Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View

Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi ; PMLR 37:428-437

Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains

Katharina Blechschmidt, Joachim Giesen, Soeren Laue ; PMLR 37:438-447

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, Christian Szegedy ; PMLR 37:448-456

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds

Yuchen Zhang, Martin Wainwright, Michael Jordan ; PMLR 37:457-465

Landmarking Manifolds with Gaussian Processes

Dawen Liang, John Paisley ; PMLR 37:466-474

Markov Mixed Membership Models

Aonan Zhang, John Paisley ; PMLR 37:475-483

A Unified Framework for Outlier-Robust PCA-like Algorithms

Wenzhuo Yang, Huan Xu ; PMLR 37:484-493

Streaming Sparse Principal Component Analysis

Wenzhuo Yang, Huan Xu ; PMLR 37:494-503

A Divide and Conquer Framework for Distributed Graph Clustering

Wenzhuo Yang, Huan Xu ; PMLR 37:504-513

How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?

Senjian An, Farid Boussaid, Mohammed Bennamoun ; PMLR 37:514-523

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning

K. Lakshmanan, Ronald Ortner, Daniil Ryabko ; PMLR 37:524-532

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

Michael Betancourt ; PMLR 37:533-540

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

Dan Garber, Elad Hazan ; PMLR 37:541-549

Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models

Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya ; PMLR 37:550-559

Online Learning of Eigenvectors

Dan Garber, Elad Hazan, Tengyu Ma ; PMLR 37:560-568

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 ; PMLR 37:569-578

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 ; PMLR 37:579-587

Ordinal Mixed Membership Models

Seppo Virtanen, Mark Girolami ; PMLR 37:588-596

Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network

Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han ; PMLR 37:597-606

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola ; PMLR 37:607-616

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares

Garvesh Raskutti, Michael Mahoney ; PMLR 37:617-625

On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence

Nathaniel Korda, Prashanth La ; PMLR 37:626-634

Learning Parametric-Output HMMs with Two Aliased States

Roi Weiss, Boaz Nadler ; PMLR 37:635-644

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Yarin Gal, Yutian Chen, Zoubin Ghahramani ; PMLR 37:645-654

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

Yarin Gal, Richard Turner ; PMLR 37:655-664

Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top

Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal ; PMLR 37:665-673

Stochastic Dual Coordinate Ascent with Adaptive Probabilities

Dominik Csiba, Zheng Qu, Peter Richtarik ; PMLR 37:674-683

Vector-Space Markov Random Fields via Exponential Families

Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar ; PMLR 37:684-692

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka ; PMLR 37:693-701

Low Rank Approximation using Error Correcting Coding Matrices

Shashanka Ubaru, Arya Mazumdar, Yousef Saad ; PMLR 37:702-710

Off-policy Model-based Learning under Unknown Factored Dynamics

Assaf Hallak, Francois Schnitzler, Timothy Mann, Shie Mannor ; PMLR 37:711-719

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 ; PMLR 37:720-729

Asymmetric Transfer Learning with Deep Gaussian Processes

Melih Kandemir ; PMLR 37:730-738

Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing

Rongda Zhu, Quanquan Gu ; PMLR 37:739-747

BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

Stephan Gouws, Yoshua Bengio, Greg Corrado ; PMLR 37:748-756

Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization

Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi ; PMLR 37:757-766

Cascading Bandits: Learning to Rank in the Cascade Model

Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan ; PMLR 37:767-776

Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models

James Foulds, Shachi Kumar, Lise Getoor ; PMLR 37:777-786

Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions

Alina Ene, Huy Nguyen ; PMLR 37:787-795

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Karthik Narayan, Ali Punjani, Pieter Abbeel ; PMLR 37:796-804

Fictitious Self-Play in Extensive-Form Games

Johannes Heinrich, Marc Lanctot, David Silver ; PMLR 37:805-813

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

Adith Swaminathan, Thorsten Joachims ; PMLR 37:814-823

The Hedge Algorithm on a Continuum

Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen ; PMLR 37:824-832

A Linear Dynamical System Model for Text

David Belanger, Sham Kakade ; PMLR 37:833-842

Unsupervised Learning of Video Representations using LSTMs

Nitish Srivastava, Elman Mansimov, Ruslan Salakhudinov ; PMLR 37:843-852

Message Passing for Collective Graphical Models

Tao Sun, Dan Sheldon, Akshat Kumar ; PMLR 37:853-861

DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics

Yining Wang, Jun Zhu ; PMLR 37:862-870

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu ; PMLR 37:871-880

MADE: Masked Autoencoder for Distribution Estimation

Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle ; PMLR 37:881-889

An Online Learning Algorithm for Bilinear Models

Yuanbin Wu, Shiliang Sun ; PMLR 37:890-898

Adaptive Belief Propagation

Georgios Papachristoudis, John Fisher ; PMLR 37:899-907

Large-scale log-determinant computation through stochastic Chebyshev expansions

Insu Han, Dmitry Malioutov, Jinwoo Shin ; PMLR 37:908-917

Differentially Private Bayesian Optimization

Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger ; PMLR 37:918-927

A Nearly-Linear Time Framework for Graph-Structured Sparsity

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt ; PMLR 37:928-937

Support Matrix Machines

Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li ; PMLR 37:938-947

Rademacher Observations, Private Data, and Boosting

Richard Nock, Giorgio Patrini, Arik Friedman ; PMLR 37:948-956

From Word Embeddings To Document Distances

Matt Kusner, Yu Sun, Nicholas Kolkin, Kilian Weinberger ; PMLR 37:957-966

Bayesian and Empirical Bayesian Forests

Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle ; PMLR 37:967-976

Inferring Graphs from Cascades: A Sparse Recovery Framework

Jean Pouget-Abadie, Thibaut Horel ; PMLR 37:977-986

Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM

Ching-Pei Lee, Dan Roth ; PMLR 37:987-996

Safe Exploration for Optimization with Gaussian Processes

Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause ; PMLR 37:997-1005

The Ladder: A Reliable Leaderboard for Machine Learning Competitions

Avrim Blum, Moritz Hardt ; PMLR 37:1006-1014

Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)

Maurizio Filippone, Raphael Engler ; PMLR 37:1015-1024

Finding Galaxies in the Shadows of Quasars with Gaussian Processes

Roman Garnett, Shirley Ho, Jeff Schneider ; PMLR 37:1025-1033

Following the Perturbed Leader for Online Structured Learning

Alon Cohen, Tamir Hazan ; PMLR 37:1034-1042

Reified Context Models

Jacob Steinhardt, Percy Liang ; PMLR 37:1043-1052

Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing

Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek ; PMLR 37:1053-1062

Learning Fast-Mixing Models for Structured Prediction

Jacob Steinhardt, Percy Liang ; PMLR 37:1063-1072

A Probabilistic Model for Dirty Multi-task Feature Selection

Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani ; PMLR 37:1073-1082

On Deep Multi-View Representation Learning

Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes ; PMLR 37:1083-1092

Learning Program Embeddings to Propagate Feedback on Student Code

Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas ; PMLR 37:1093-1102

Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems

Qiang Zhou, Qi Zhao ; PMLR 37:1103-1112

Efficient Learning in Large-Scale Combinatorial Semi-Bandits

Zheng Wen, Branislav Kveton, Azin Ashkan ; PMLR 37:1113-1122

Swept Approximate Message Passing for Sparse Estimation

Andre Manoel, Florent Krzakala, Eric Tramel, Lenka Zdeborovà ; PMLR 37:1123-1132

Simple regret for infinitely many armed bandits

Alexandra Carpentier, Michal Valko ; PMLR 37:1133-1141

Exponential Integration for Hamiltonian Monte Carlo

Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha ; PMLR 37:1142-1151

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa ; PMLR 37:1152-1161

Faster cover trees

Mike Izbicki, Christian Shelton ; PMLR 37:1162-1170

Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization

Tyler Johnson, Carlos Guestrin ; PMLR 37:1171-1179

Unsupervised Domain Adaptation by Backpropagation

Yaroslav Ganin, Victor Lempitsky ; PMLR 37:1180-1189

Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer

Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu ; PMLR 37:1190-1198

Manifold-valued Dirichlet Processes

Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh ; PMLR 37:1199-1208

Multi-Task Learning for Subspace Segmentation

Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell ; PMLR 37:1209-1217

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Tim Salimans, Diederik Kingma, Max Welling ; PMLR 37:1218-1226

Scalable Model Selection for Large-Scale Factorial Relational Models

Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka ; PMLR 37:1227-1235

The Power of Randomization: Distributed Submodular Maximization on Massive Datasets

Rafael Barbosa, Alina Ene, Huy Nguyen, Justin Ward ; PMLR 37:1236-1244

Dealing with small data: On the generalization of context trees

Ralf Eggeling, Mikko Koivisto, Ivo Grosse ; PMLR 37:1245-1253

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood

Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin ; PMLR 37:1254-1263

A Bayesian nonparametric procedure for comparing algorithms

Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon ; PMLR 37:1264-1272

Convergence rate of Bayesian tensor estimator and its minimax optimality

Taiji Suzuki ; PMLR 37:1273-1282

On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments

Yifan Wu, Andras Gyorgy, Csaba Szepesvari ; PMLR 37:1283-1291

Nested Sequential Monte Carlo Methods

Christian Naesseth, Fredrik Lindsten, Thomas Schon ; PMLR 37:1292-1301

Sparse Variational Inference for Generalized GP Models

Rishit Sheth, Yuyang Wang, Roni Khardon ; PMLR 37:1302-1311

Universal Value Function Approximators

Tom Schaul, Daniel Horgan, Karol Gregor, David Silver ; PMLR 37:1312-1320

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games

Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin ; PMLR 37:1321-1329

On Greedy Maximization of Entropy

Dravyansh Sharma, Ashish Kapoor, Amit Deshpande ; PMLR 37:1330-1338

Metadata Dependent Mondrian Processes

Yi Wang, Bin Li, Yang Wang, Fang Chen ; PMLR 37:1339-1347

Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM

Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu ; PMLR 37:1348-1357

Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood

Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki ; PMLR 37:1358-1366

Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets

Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung ; PMLR 37:1367-1375

The Composition Theorem for Differential Privacy

Peter Kairouz, Sewoong Oh, Pramod Viswanath ; PMLR 37:1376-1385

Convex Formulation for Learning from Positive and Unlabeled Data

Marthinus Du Plessis, Gang Niu, Masashi Sugiyama ; PMLR 37:1386-1394

Threshold Influence Model for Allocating Advertising Budgets

Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura ; PMLR 37:1395-1404

Strongly Adaptive Online Learning

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz ; PMLR 37:1405-1411

CUR Algorithm for Partially Observed Matrices

Miao Xu, Rong Jin, Zhi-Hua Zhou ; PMLR 37:1412-1421

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data

Yining Wang, Yu-Xiang Wang, Aarti Singh ; PMLR 37:1422-1431

MRA-based Statistical Learning from Incomplete Rankings

Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz ; PMLR 37:1432-1441

Risk and Regret of Hierarchical Bayesian Learners

Jonathan Huggins, Josh Tenenbaum ; PMLR 37:1442-1451

Towards a Learning Theory of Cause-Effect Inference

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin ; PMLR 37:1452-1461

DRAW: A Recurrent Neural Network For Image Generation

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra ; PMLR 37:1462-1471

Multiview Triplet Embedding: Learning Attributes in Multiple Maps

Ehsan Amid, Antti Ukkonen ; PMLR 37:1472-1480

Distributed Gaussian Processes

Marc Deisenroth, Jun Wei Ng ; PMLR 37:1481-1490

Guaranteed Tensor Decomposition: A Moment Approach

Gongguo Tang, Parikshit Shah ; PMLR 37:1491-1500

\ell_1,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods

Zirui Zhou, Qi Zhang, Anthony Man-Cho So ; PMLR 37:1501-1510

Consistent estimation of dynamic and multi-layer block models

Qiuyi Han, Kevin Xu, Edoardo Airoldi ; PMLR 37:1511-1520

On the Rate of Convergence and Error Bounds for LSTD(λ)

Manel Tagorti, Bruno Scherrer ; PMLR 37:1521-1529

Variational Inference with Normalizing Flows

Danilo Rezende, Shakir Mohamed ; PMLR 37:1530-1538

Controversy in mechanistic modelling with Gaussian processes

Benn Macdonald, Catherine Higham, Dirk Husmeier ; PMLR 37:1539-1547

Convex Learning of Multiple Tasks and their Structure

Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco ; PMLR 37:1548-1557

K-hyperplane Hinge-Minimax Classifier

Margarita Osadchy, Tamir Hazan, Daniel Keren ; PMLR 37:1558-1566

Non-Stationary Approximate Modified Policy Iteration

Boris Lesner, Bruno Scherrer ; PMLR 37:1567-1575

Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees

Mathieu Serrurier, Henri Prade ; PMLR 37:1576-1584

Geometric Conditions for Subspace-Sparse Recovery

Chong You, Rene Vidal ; PMLR 37:1585-1593

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process

Amar Shah, David Knowles, Zoubin Ghahramani ; PMLR 37:1594-1603

Long Short-Term Memory Over Recursive Structures

Xiaodan Zhu, Parinaz Sobihani, Hongyu Guo ; PMLR 37:1604-1612

Weight Uncertainty in Neural Network

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra ; PMLR 37:1613-1622

Learning Submodular Losses with the Lovasz Hinge

Jiaqian Yu, Matthew Blaschko ; PMLR 37:1623-1631

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

Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke ; PMLR 37:1632-1641

Hashing for Distributed Data

Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu ; PMLR 37:1642-1650

Large-scale Distributed Dependent Nonparametric Trees

Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing ; PMLR 37:1651-1659

Qualitative Multi-Armed Bandits: A Quantile-Based Approach

Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier ; PMLR 37:1660-1668

Deep Edge-Aware Filters

Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia ; PMLR 37:1669-1678

A Convex Optimization Framework for Bi-Clustering

Shiau Hong Lim, Yudong Chen, Huan Xu ; PMLR 37:1679-1688

Is Feature Selection Secure against Training Data Poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli ; PMLR 37:1689-1698

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani ; PMLR 37:1699-1707

A Theoretical Analysis of Metric Hypothesis Transfer Learning

Michaël Perrot, Amaury Habrard ; PMLR 37:1708-1717

Generative Moment Matching Networks

Yujia Li, Kevin Swersky, Rich Zemel ; PMLR 37:1718-1727

Stay on path: PCA along graph paths

Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran ; PMLR 37:1728-1736

Deep Learning with Limited Numerical Precision

Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan ; PMLR 37:1737-1746

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

Jie Wang, Jieping Ye ; PMLR 37:1747-1756

Harmonic Exponential Families on Manifolds

Taco Cohen, Max Welling ; PMLR 37:1757-1765

Training Deep Convolutional Neural Networks to Play Go

Christopher Clark, Amos Storkey ; PMLR 37:1766-1774

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

Andrew Wilson, Hannes Nickisch ; PMLR 37:1775-1784

Learning Deep Structured Models

Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun ; PMLR 37:1785-1794

Community Detection Using Time-Dependent Personalized PageRank

Haim Avron, Lior Horesh ; PMLR 37:1795-1803

Scalable Variational Inference in Log-supermodular Models

Josip Djolonga, Andreas Krause ; PMLR 37:1804-1813

Variational Inference for Gaussian Process Modulated Poisson Processes

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts ; PMLR 37:1814-1822

Scalable Deep Poisson Factor Analysis for Topic Modeling

Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin ; PMLR 37:1823-1832

Hidden Markov Anomaly Detection

Nico Goernitz, Mikio Braun, Marius Kloft ; PMLR 37:1833-1842

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

Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo ; PMLR 37:1843-1851

Convex Calibrated Surrogates for Hierarchical Classification

Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal ; PMLR 37:1852-1860

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Jose Miguel Hernandez-Lobato, Ryan Adams ; PMLR 37:1861-1869

Active Nearest Neighbors in Changing Environments

Christopher Berlind, Ruth Urner ; PMLR 37:1870-1879

Bipartite Edge Prediction via Transductive Learning over Product Graphs

Hanxiao Liu, Yiming Yang ; PMLR 37:1880-1888

Trust Region Policy Optimization

John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz ; PMLR 37:1889-1897

Discovering Temporal Causal Relations from Subsampled Data

Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger ; PMLR 37:1898-1906

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon ; PMLR 37:1907-1916

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing ; PMLR 37:1917-1925

On Symmetric and Asymmetric LSHs for Inner Product Search

Behnam Neyshabur, Nathan Srebro ; PMLR 37:1926-1934

The Kendall and Mallows Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert ; PMLR 37:1935-1944

Bayesian Multiple Target Localization

Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak ; PMLR 37:1945-1953

Submodularity in Data Subset Selection and Active Learning

Kai Wei, Rishabh Iyer, Jeff Bilmes ; PMLR 37:1954-1963

Variational Generative Stochastic Networks with Collaborative Shaping

Philip Bachman, Doina Precup ; PMLR 37:1964-1972

Adding vs. Averaging in Distributed Primal-Dual Optimization

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac ; PMLR 37:1973-1982

Feature-Budgeted Random Forest

Feng Nan, Joseph Wang, Venkatesh Saligrama ; PMLR 37:1983-1991

Entropic Graph-based Posterior Regularization

Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble ; PMLR 37:1992-2001

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations

Tam Le, Marco Cuturi ; PMLR 37:2002-2011

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

Or Zuk, Avishai Wagner ; PMLR 37:2012-2020

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 ; PMLR 37:2021-2029

A Multitask Point Process Predictive Model

Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin ; PMLR 37:2030-2038

A Hybrid Approach for Probabilistic Inference using Random Projections

Michael Zhu, Stefano Ermon ; PMLR 37:2039-2047

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 ; PMLR 37:2048-2057

Learning to Search Better than Your Teacher

Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume, John Langford ; PMLR 37:2058-2066

Gated Feedback Recurrent Neural Networks

Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio ; PMLR 37:2067-2075

Context-based Unsupervised Data Fusion for Decision Making

Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela Schaar ; PMLR 37:2076-2084

Phrase-based Image Captioning

Remi Lebret, Pedro Pinheiro, Ronan Collobert ; PMLR 37:2085-2094

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 ; PMLR 37:2095-2103

Distributional Rank Aggregation, and an Axiomatic Analysis

Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar ; PMLR 37:2104-2112

Gradient-based Hyperparameter Optimization through Reversible Learning

Dougal Maclaurin, David Duvenaud, Ryan Adams ; PMLR 37:2113-2122

Bimodal Modelling of Source Code and Natural Language

Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei ; PMLR 37:2123-2132

Cheap Bandits

Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Remi Munos ; PMLR 37:2133-2142

Subsampling Methods for Persistent Homology

Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry Wasserman ; PMLR 37:2143-2151

An embarrassingly simple approach to zero-shot learning

Bernardino Romera-Paredes, Philip Torr ; PMLR 37:2152-2161

Binary Embedding: Fundamental Limits and Fast Algorithm

Xinyang Yi, Constantine Caramanis, Eric Price ; PMLR 37:2162-2170

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 ; PMLR 37:2171-2180

How Hard is Inference for Structured Prediction?

Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim ; PMLR 37:2181-2190

Online Time Series Prediction with Missing Data

Oren Anava, Elad Hazan, Assaf Zeevi ; PMLR 37:2191-2199

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

Jason Pacheco, Erik Sudderth ; PMLR 37:2200-2208

A Fast Variational Approach for Learning Markov Random Field Language Models

Yacine Jernite, Alexander Rush, David Sontag ; PMLR 37:2209-2217

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 ; PMLR 37:2218-2226

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

Yves-Laurent Kom Samo, Stephen Roberts ; PMLR 37:2227-2236

Correlation Clustering in Data Streams

KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth ; PMLR 37:2237-2246

Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

Qingming Tang, Siqi Sun, Jinbo Xu ; PMLR 37:2247-2255

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli ; PMLR 37:2256-2265

Modeling Order in Neural Word Embeddings at Scale

Andrew Trask, David Gilmore, Matthew Russell ; PMLR 37:2266-2275

Distributed Inference for Dirichlet Process Mixture Models

Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani ; PMLR 37:2276-2284

Compressing Neural Networks with the Hashing Trick

Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen ; PMLR 37:2285-2294

Intersecting Faces: Non-negative Matrix Factorization With New Guarantees

Rong Ge, James Zou ; PMLR 37:2295-2303

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix

Roger Grosse, Ruslan Salakhudinov ; PMLR 37:2304-2313

A Deeper Look at Planning as Learning from Replay

Harm Vanseijen, Rich Sutton ; PMLR 37:2314-2322

Optimal and Adaptive Algorithms for Online Boosting

Alina Beygelzimer, Satyen Kale, Haipeng Luo ; PMLR 37:2323-2331

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

Christopher De Sa, Christopher Re, Kunle Olukotun ; PMLR 37:2332-2341

An Empirical Exploration of Recurrent Network Architectures

Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever ; PMLR 37:2342-2350

Complete Dictionary Recovery Using Nonconvex Optimization

Ju Sun, Qing Qu, John Wright ; PMLR 37:2351-2360

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

Haitham Bou Ammar, Rasul Tutunov, Eric Eaton ; PMLR 37:2361-2369

PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent

Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon ; PMLR 37:2370-2379

High Confidence Policy Improvement

Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh ; PMLR 37:2380-2388

Fixed-point algorithms for learning determinantal point processes

Zelda Mariet, Suvrit Sra ; PMLR 37:2389-2397

Consistent Multiclass Algorithms for Complex Performance Measures

Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal ; PMLR 37:2398-2407

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

James Martens, Roger Grosse ; PMLR 37:2408-2417

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

En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon ; PMLR 37:2418-2426

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

Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga ; PMLR 37:2427-2435

Entropy-Based Concentration Inequalities for Dependent Variables

Liva Ralaivola, Massih-Reza Amini ; PMLR 37:2436-2444

PU Learning for Matrix Completion

Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon ; PMLR 37:2445-2453

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization

Necdet Aybat, Zi Wang, Garud Iyengar ; PMLR 37:2454-2462

Sparse Subspace Clustering with Missing Entries

Congyuan Yang, Daniel Robinson, Rene Vidal ; PMLR 37:2463-2472

Moderated and Drifting Linear Dynamical Systems

Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler, Kobus Barnard ; PMLR 37:2473-2482

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions

Taehoon Lee, Sungroh Yoon ; PMLR 37:2483-2492

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

Yu-Xiang Wang, Stephen Fienberg, Alex Smola ; PMLR 37:2493-2502

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

Lucas Theis, Matt Hoffman ; PMLR 37:2503-2511

Inference in a Partially Observed Queuing Model with Applications in Ecology

Kevin Winner, Garrett Bernstein, Dan Sheldon ; PMLR 37:2512-2520

Deterministic Independent Component Analysis

Ruitong Huang, Andras Gyorgy, Csaba Szepesvári ; PMLR 37:2521-2530

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

Maxime Gasse, Alexandre Aussem, Haytham Elghazel ; PMLR 37:2531-2539

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

Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford ; PMLR 37:2540-2548

A New Generalized Error Path Algorithm for Model Selection

Bin Gu, Charles Ling ; PMLR 37:2549-2558

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