JMLR Volume 17

On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier; 17(1):1−42, 2016.
[abs][pdf][bib]

Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness
Mauro Maggioni, Stanislav Minsker, Nate Strawn; 17(2):1−51, 2016.
[abs][pdf][bib]

Consistent Algorithms for Clustering Time Series
Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux; 17(3):1−32, 2016.
[abs][pdf][bib]

Random Rotation Ensembles
Rico Blaser, Piotr Fryzlewicz; 17(4):1−26, 2016.
[abs][pdf][bib]

Should We Really Use Post-Hoc Tests Based on Mean-Ranks?
Alessio Benavoli, Giorgio Corani, Francesca Mangili; 17(5):1−10, 2016.
[abs][pdf][bib]

Minimax Rates in Permutation Estimation for Feature Matching
Olivier Collier, Arnak S. Dalalyan; 17(6):1−31, 2016.
[abs][pdf][bib]

Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer; 17(7):1−33, 2016.
[abs][pdf][bib]

Knowledge Matters: Importance of Prior Information for Optimization
Çağlar Gülçehre, Yoshua Bengio; 17(8):1−32, 2016.
[abs][pdf][bib]

Harry: A Tool for Measuring String Similarity
Konrad Rieck, Christian Wressnegger; 17(9):1−5, 2016.
[abs][pdf][bib]    [code][mlsec.org]

Herded Gibbs Sampling
Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling; 17(10):1−29, 2016.
[abs][pdf][bib]

Complexity of Representation and Inference in Compositional Models with Part Sharing
Alan Yuille, Roozbeh Mottaghi; 17(11):1−28, 2016.
[abs][pdf][bib]

Noisy Sparse Subspace Clustering
Yu-Xiang Wang, Huan Xu; 17(12):1−41, 2016.
[abs][pdf][bib]

Learning the Variance of the Reward-To-Go
Aviv Tamar, Dotan Di Castro, Shie Mannor; 17(13):1−36, 2016.
[abs][pdf][bib]

Convex Calibration Dimension for Multiclass Loss Matrices
Harish G. Ramaswamy, Shivani Agarwal; 17(14):1−45, 2016.
[abs][pdf][bib]

LLORMA: Local Low-Rank Matrix Approximation
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio; 17(15):1−24, 2016.
[abs][pdf][bib]

A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces
Xiang Zhang, Yichao Wu, Lan Wang, Runze Li; 17(16):1−26, 2016.
[abs][pdf][bib]

Extremal Mechanisms for Local Differential Privacy
Peter Kairouz, Sewoong Oh, Pramod Viswanath; 17(17):1−51, 2016.
[abs][pdf][bib]

Loss Minimization and Parameter Estimation with Heavy Tails
Daniel Hsu, Sivan Sabato; 17(18):1−40, 2016.
[abs][pdf][bib]

Analysis of Classification-based Policy Iteration Algorithms
Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos; 17(19):1−30, 2016.
[abs][pdf][bib]

Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren; 17(20):1−54, 2016.
[abs][pdf][bib]

MEKA: A Multi-label/Multi-target Extension to WEKA
Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; 17(21):1−5, 2016.
[abs][pdf][bib]    [code][sourceforge]

Gradients Weights improve Regression and Classification
Samory Kpotufe, Abdeslam Boularias, Thomas Schultz, Kyoungok Kim; 17(22):1−34, 2016.
[abs][pdf][bib]

A Closer Look at Adaptive Regret
Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, Vladimir Vovk; 17(23):1−21, 2016.
[abs][pdf][bib]

Learning Using Anti-Training with Sacrificial Data
Michael L. Valenzuela, Jerzy W. Rozenblit; 17(24):1−42, 2016.
[abs][pdf][bib]

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
Hà Quang Minh, Loris Bazzani, Vittorio Murino; 17(25):1−72, 2016.
[abs][pdf][bib]

Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Lucas Mentch, Giles Hooker; 17(26):1−41, 2016.
[abs][pdf][bib]

Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Yudong Chen, Jiaming Xu; 17(27):1−57, 2016.
[abs][pdf][bib]

Non-linear Causal Inference using Gaussianity Measures
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez; 17(28):1−39, 2016.
[abs][pdf][bib]    [appendix]

Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables
Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, Malka Gorfine; 17(29):1−54, 2016.
[abs][pdf][bib]

A Gibbs Sampler for Learning DAGs
Robert J. B. Goudie, Sach Mukherjee; 17(30):1−39, 2016.
[abs][pdf][bib]

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
Francois Denis, Mattias Gybels, Amaury Habrard; 17(31):1−32, 2016.
[abs][pdf][bib]

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf; 17(32):1−102, 2016.
[abs][pdf][bib]    [appendix]

Multi-task Sparse Structure Learning with Gaussian Copula Models
André R. Goncalves, Fernando J. Von Zuben, Arindam Banerjee; 17(33):1−30, 2016.
[abs][pdf][bib]

MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; 17(34):1−7, 2016.
[abs][pdf][bib]    [code][apache.org]

OLPS: A Toolbox for On-Line Portfolio Selection
Bin Li, Doyen Sahoo, Steven C.H. Hoi; 17(35):1−5, 2016.
[abs][pdf][bib]    [code][github]

A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors
Julianus Pfeuffer, Oliver Serang; 17(36):1−39, 2016.
[abs][pdf][bib]

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks
Shiliang Zhang, Hui Jiang, Lirong Dai; 17(37):1−33, 2016.
[abs][pdf][bib]

The Optimal Sample Complexity of PAC Learning
Steve Hanneke; 17(38):1−15, 2016.
[abs][pdf][bib]

End-to-End Training of Deep Visuomotor Policies
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel; 17(39):1−40, 2016.
[abs][pdf][bib]

On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint
Chong Zhang, Yufeng Liu, Yichao Wu; 17(40):1−45, 2016.
[abs][pdf][bib]

BayesPy: Variational Bayesian Inference in Python
Jaakko Luttinen; 17(41):1−6, 2016.
[abs][pdf][bib]    [appendix]    [code][bayespy.org]

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence; 17(42):1−62, 2016.
[abs][pdf][bib]

On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
Ery Arias-Castro, David Mason, Bruno Pelletier; 17(43):1−28, 2016.
[abs][pdf][bib]

Scalable Learning of Bayesian Network Classifiers
Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar A. Zaidi; 17(44):1−35, 2016.
[abs][pdf][bib]

A Unified View on Multi-class Support Vector Classification
Ürün Doğan, Tobias Glasmachers, Christian Igel; 17(45):1−32, 2016.
[abs][pdf][bib]    [code][Supplement]

Addressing Environment Non-Stationarity by Repeating Q-learning Updates
Sherief Abdallah, Michael Kaisers; 17(46):1−31, 2016.
[abs][pdf][bib]

Large Scale Online Kernel Learning
Jing Lu, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Zhi-Yong Liu; 17(47):1−43, 2016.
[abs][pdf][bib]

Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf; 17(48):1−41, 2016.
[abs][pdf][bib]

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
Shusen Wang, Luo Luo, Zhihua Zhang; 17(49):1−49, 2016.
[abs][pdf][bib]

Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang; 17(50):1−33, 2016.
[abs][pdf][bib]

Differentially Private Data Releasing for Smooth Queries
Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang; 17(51):1−42, 2016.
[abs][pdf][bib]

Subspace Learning with Partial Information
Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz; 17(52):1−21, 2016.
[abs][pdf][bib]

Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
Mert Pilanci, Martin J. Wainwright; 17(53):1−38, 2016.
[abs][pdf][bib]

Estimating Causal Structure Using Conditional DAG Models
Chris. J. Oates, Jim Q. Smith, Sach Mukherjee; 17(54):1−23, 2016.
[abs][pdf][bib]

Adaptive Lasso and group-Lasso for functional Poisson regression
Stéphane Ivanoff, Franck Picard, Vincent Rivoirard; 17(55):1−46, 2016.
[abs][pdf][bib]

Causal Inference through a Witness Protection Program
Ricardo Silva, Robin Evans; 17(56):1−53, 2016.
[abs][pdf][bib]

Structure Discovery in Bayesian Networks by Sampling Partial Orders
Teppo Niinimäki, Pekka Parviainen, Mikko Koivisto; 17(57):1−47, 2016.
[abs][pdf][bib]

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright; 17(58):1−47, 2016.
[abs][pdf][bib]

Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky; 17(59):1−35, 2016.
[abs][pdf][bib]

Probabilistic Low-Rank Matrix Completion from Quantized Measurements
Sonia A. Bhaskar; 17(60):1−34, 2016.
[abs][pdf][bib]

DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
Aryan Mokhtari, Alejandro Ribeiro; 17(61):1−35, 2016.
[abs][pdf][bib]

The Statistical Performance of Collaborative Inference
Gérard Biau, Kevin Bleakley, Benoît Cadre; 17(62):1−29, 2016.
[abs][pdf][bib]

Convergence of an Alternating Maximization Procedure
Andreas Andresen, Vladimir Spokoiny; 17(63):1−53, 2016.
[abs][pdf][bib]

StructED: Risk Minimization in Structured Prediction
Yossi Adi, Joseph Keshet; 17(64):1−5, 2016.
[abs][pdf][bib]    [code][github]

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Žbontar, Yann LeCun; 17(65):1−32, 2016.
[abs][pdf][bib]    [code][github]

Bayesian Policy Gradient and Actor-Critic Algorithms
Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko; 17(66):1−53, 2016.
[abs][pdf][bib]

Practical Kernel-Based Reinforcement Learning
André M.S. Barreto, Doina Precup, Joelle Pineau; 17(67):1−70, 2016.
[abs][pdf][bib]

An Information-Theoretic Analysis of Thompson Sampling
Daniel Russo, Benjamin Van Roy; 17(68):1−30, 2016.
[abs][pdf][bib]

Compressed Gaussian Process for Manifold Regression
Rajarshi Guhaniyogi, David B. Dunson; 17(69):1−26, 2016.
[abs][pdf][bib]

On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting
Matey Neykov, Jun S. Liu, Tianxi Cai; 17(70):1−32, 2016.
[abs][pdf][bib]

Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums
P.-L. Giscard, Z. Choo, S. J. Thwaite, D. Jaksch; 17(71):1−19, 2016.
[abs][pdf][bib]

Challenges in multimodal gesture recognition
Sergio Escalera, Vassilis Athitsos, Isabelle Guyon; 17(72):1−54, 2016.
[abs][pdf][bib]

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning
Richard S. Sutton, A. Rupam Mahmood, Martha White; 17(73):1−29, 2016.
[abs][pdf][bib]

Learning Algorithms for Second-Price Auctions with Reserve
Mehryar Mohri, Andres Munoz Medina; 17(74):1−25, 2016.
[abs][pdf][bib]

Distributed Coordinate Descent Method for Learning with Big Data
Peter Richtárik, Martin Takáč; 17(75):1−25, 2016.
[abs][pdf][bib]

Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
Stephan Clémençon, Igor Colin, Aurélien Bellet; 17(76):1−36, 2016.
[abs][pdf][bib]

Iterative Regularization for Learning with Convex Loss Functions
Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou; 17(77):1−38, 2016.
[abs][pdf][bib]

Latent Space Inference of Internet-Scale Networks
Qirong Ho, Junming Yin, Eric P. Xing; 17(78):1−41, 2016.
[abs][pdf][bib]

Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
Jenna Wiens, John Guttag, Eric Horvitz; 17(79):1−23, 2016.
[abs][pdf][bib]

Multiplicative Multitask Feature Learning
Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song; 17(80):1−33, 2016.
[abs][pdf][bib]

The Benefit of Multitask Representation Learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes; 17(81):1−32, 2016.
[abs][pdf][bib]

Model-free Variable Selection in Reproducing Kernel Hilbert Space
Lei Yang, Shaogao Lv, Junhui Wang; 17(82):1−24, 2016.
[abs][pdf][bib]

CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Steven Diamond, Stephen Boyd; 17(83):1−5, 2016.
[abs][pdf][bib]    [code][cvxpy.org]

Lenient Learning in Independent-Learner Stochastic Cooperative Games
Ermo Wei, Sean Luke; 17(84):1−42, 2016.
[abs][pdf][bib]

Structure-Leveraged Methods in Breast Cancer Risk Prediction
Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy Peissig, Elizabeth Burnside; 17(85):1−15, 2016.
[abs][pdf][bib]

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems
Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; 17(86):1−5, 2016.
[abs][pdf][bib]    [code][ntu.edu.tw]

L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
Matey Neykov, Jun S. Liu, Tianxi Cai; 17(87):1−37, 2016.
[abs][pdf][bib]

Spectral Ranking using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic; 17(88):1−45, 2016.
[abs][pdf][bib]

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
Xin Guo, Jun Fan, Ding-Xuan Zhou; 17(89):1−34, 2016.
[abs][pdf][bib]

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm
Manuel Gomez-Rodriguez, Le Song, Hadi Daneshmand, Bernhard Schölkopf; 17(90):1−29, 2016.
[abs][pdf][bib]

Rounding-based Moves for Semi-Metric Labeling
M. Pawan Kumar, Puneet K. Dokania; 17(91):1−42, 2016.
[abs][pdf][bib]

Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices
Dan Yang, Zongming Ma, Andreas Buja; 17(92):1−27, 2016.
[abs][pdf][bib]

Hierarchical Relative Entropy Policy Search
Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters; 17(93):1−50, 2016.
[abs][pdf][bib]

Convex Regression with Interpretable Sharp Partitions
Ashley Petersen, Noah Simon, Daniela Witten; 17(94):1−31, 2016.
[abs][pdf][bib]

JCLAL: A Java Framework for Active Learning
Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura; 17(95):1−5, 2016.
[abs][pdf][bib]    [code][sourceforge]

Integrated Common Sense Learning and Planning in POMDPs
Brendan Juba; 17(96):1−37, 2016.
[abs][pdf][bib]

Cells in Multidimensional Recurrent Neural Networks
Gundram Leifert, Tobias Strauβ, Tobias Grüning, Welf Wustlich, Roger Labahn; 17(97):1−37, 2016.
[abs][pdf][bib]

Learning Taxonomy Adaptation in Large-scale Classification
Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard; 17(98):1−37, 2016.
[abs][pdf][bib]

How to Center Deep Boltzmann Machines
Jan Melchior, Asja Fischer, Laurenz Wiskott; 17(99):1−61, 2016.
[abs][pdf][bib]

Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
Zijian Guo, Dylan S. Small; 17(100):1−35, 2016.
[abs][pdf][bib]

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling
Ru He, Jin Tian, Huaiqing Wu; 17(101):1−54, 2016.
[abs][pdf][bib]    [appendix]

Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan; 17(102):1−44, 2016.
[abs][pdf][bib]

Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models
Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther; 17(103):1−38, 2016.
[abs][pdf][bib]

e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem
Marcela Zuluaga, Andreas Krause, Markus Püschel; 17(104):1−32, 2016.
[abs][pdf][bib]

Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani; 17(105):1−41, 2016.
[abs][pdf][bib]

Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Randy Katz; 17(106):1−37, 2016.
[abs][pdf][bib]

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers; 17(107):1−31, 2016.
[abs][pdf][bib]

Distribution-Matching Embedding for Visual Domain Adaptation
Mahsa Baktashmotlagh, Mehrtash Harandi, Mathieu Salzmann; 17(108):1−30, 2016.
[abs][pdf][bib]

Monotonic Calibrated Interpolated Look-Up Tables
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, Alexander van Esbroeck; 17(109):1−47, 2016.
[abs][pdf][bib]

Are Random Forests Truly the Best Classifiers?
Michael Wainberg, Babak Alipanahi, Brendan J. Frey; 17(110):1−5, 2016.
[abs][pdf][bib]    [appendix]

Minimax Adaptive Estimation of Nonparametric Hidden Markov Models
Yohann De Castro, Élisabeth Gassiat, Claire Lacour; 17(111):1−43, 2016.
[abs][pdf][bib]

Decrypting ``Cryptogenic'' Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients
Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniekcy, Orrin Devinsky, Carla E. Brodley; 17(112):1−30, 2016.
[abs][pdf][bib]

Fused Lasso Approach in Regression Coefficients Clustering -- Learning Parameter Heterogeneity in Data Integration
Lu Tang, Peter X.K. Song; 17(113):1−23, 2016.
[abs][pdf][bib]

The LRP Toolbox for Artificial Neural Networks
Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek; 17(114):1−5, 2016.
[abs][pdf][bib]    [code][heatmapping.org]

Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
Somayeh Sojoudi; 17(115):1−21, 2016.
[abs][pdf][bib]

A Network That Learns Strassen Multiplication
Veit Elser; 17(116):1−13, 2016.
[abs][pdf][bib]

Revisiting the Nystrom Method for Improved Large-scale Machine Learning
Alex Gittens, Michael W. Mahoney; 17(117):1−65, 2016.
[abs][pdf][bib]

Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling
Chengwei Su, Mark E. Borsuk; 17(118):1−20, 2016.
[abs][pdf][bib]

Volumetric Spanners: An Efficient Exploration Basis for Learning
Elad Hazan, Zohar Karnin; 17(119):1−34, 2016.
[abs][pdf][bib]

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney; 17(120):1−38, 2016.
[abs][pdf][bib]

Variational Dependent Multi-output Gaussian Process Dynamical Systems
Jing Zhao, Shiliang Sun; 17(121):1−36, 2016.
[abs][pdf][bib]

Multiple Output Regression with Latent Noise
Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski; 17(122):1−35, 2016.
[abs][pdf][bib]

The Constrained Dantzig Selector with Enhanced Consistency
Yinfei Kong, Zemin Zheng, Jinchi Lv; 17(123):1−22, 2016.
[abs][pdf][bib]

Bootstrap-Based Regularization for Low-Rank Matrix Estimation
Julie Josse, Stefan Wager; 17(124):1−29, 2016.
[abs][pdf][bib]

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Michael U. Gutmann, Jukka Corander; 17(125):1−47, 2016.
[abs][pdf][bib]    [Gauss.avi][Ricker1D.avi]

On Lower and Upper Bounds in Smooth and Strongly Convex Optimization
Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir; 17(126):1−51, 2016.
[abs][pdf][bib]

Dual Control for Approximate Bayesian Reinforcement Learning
Edgar D. Klenske, Philipp Hennig; 17(127):1−30, 2016.
[abs][pdf][bib]

Multiple-Instance Learning from Distributions
Gary Doran, Soumya Ray; 17(128):1−50, 2016.
[abs][pdf][bib]

An Online Convex Optimization Approach to Blackwell's Approachability
Nahum Shimkin; 17(129):1−23, 2016.
[abs][pdf][bib]

A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
Ashwini Maurya; 17(130):1−28, 2016.
[abs][pdf][bib]

String and Membrane Gaussian Processes
Yves-Laurent Kom Samo, Stephen J. Roberts; 17(131):1−87, 2016.
[abs][pdf][bib]

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
Byron C. Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu, Iain J. Marshall; 17(132):1−25, 2016.
[abs][pdf][bib]

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez; 17(133):1−38, 2016.
[abs][pdf][bib]

Adjusting for Chance Clustering Comparison Measures
Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor; 17(134):1−32, 2016.
[abs][pdf][bib]

Refined Error Bounds for Several Learning Algorithms
Steve Hanneke; 17(135):1−55, 2016.
[abs][pdf][bib]

Synergy of Monotonic Rules
Vladimir Vapnik, Rauf Izmailov; 17(136):1−33, 2016.
[abs][pdf][bib]

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
James Townsend, Niklas Koep, Sebastian Weichwald; 17(137):1−5, 2016.
[abs][pdf][bib]    [code][github]

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum; 17(138):1−49, 2016.
[abs][pdf][bib]

Regularized Policy Iteration with Nonparametric Function Spaces
Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor; 17(139):1−66, 2016.
[abs][pdf][bib]

Multiscale Adaptive Representation of Signals: I. The Basic Framework
Cheng Tai, Weinan E; 17(140):1−38, 2016.
[abs][pdf][bib]

Sparse PCA via Covariance Thresholding
Yash Deshpande, Andrea Montanari; 17(141):1−41, 2016.
[abs][pdf][bib]

Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning
Judy Hoffman, Deepak Pathak, Eric Tzeng, Jonathan Long, Sergio Guadarrama, Trevor Darrell, Kate Saenko; 17(142):1−31, 2016.
[abs][pdf][bib]

Covariance-based Clustering in Multivariate and Functional Data Analysis
Francesca Ieva, Anna Maria Paganoni, Nicholas Tarabelloni; 17(143):1−21, 2016.
[abs][pdf][bib]

MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions
Rina Foygel Barber, Emil Y. Sidky; 17(144):1−51, 2016.
[abs][pdf][bib]

True Online Temporal-Difference Learning
Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton; 17(145):1−40, 2016.
[abs][pdf][bib]

Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models
Jiahe Lin, Sumanta Basu, Moulinath Banerjee, George Michailidis; 17(146):1−51, 2016.
[abs][pdf][bib]

Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means
Twan van Laarhoven, Elena Marchiori; 17(147):1−28, 2016.
[abs][pdf][bib]

Megaman: Scalable Manifold Learning in Python
James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang; 17(148):1−5, 2016.
[abs][pdf][bib]    [code][github]

Kernel Estimation and Model Combination in A Bandit Problem with Covariates
Wei Qian, Yuhong Yang; 17(149):1−37, 2016.
[abs][pdf][bib]

A General Framework for Consistency of Principal Component Analysis
Dan Shen, Haipeng Shen, J. S. Marron; 17(150):1−34, 2016.
[abs][pdf][bib]

Conditional Independencies under the Algorithmic Independence of Conditionals
Jan Lemeire; 17(151):1−20, 2016.
[abs][pdf][bib]

Learning Theory for Distribution Regression
Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton; 17(152):1−40, 2016.
[abs][pdf][bib]

A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Weijie Su, Stephen Boyd, Emmanuel J. Candès; 17(153):1−43, 2016.
[abs][pdf][bib]

Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits
Gergely Neu, Gábor Bartók; 17(154):1−21, 2016.
[abs][pdf][bib]

New Perspectives on k-Support and Cluster Norms
Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos; 17(155):1−38, 2016.
[abs][pdf][bib]

Minimum Density Hyperplanes
Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis; 17(156):1−33, 2016.
[abs][pdf][bib]

Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
Alberto N. Escalante-B., Laurenz Wiskott; 17(157):1−36, 2016.
[abs][pdf][bib]

Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine
Simon Odense, Roderick Edwards; 17(158):1−21, 2016.
[abs][pdf][bib]

Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics
Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh; 17(159):1−48, 2016.
[abs][pdf][bib]

A General Framework for Constrained Bayesian Optimization using Information-based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani; 17(160):1−53, 2016.
[abs][pdf][bib]

Optimal Estimation and Completion of Matrices with Biclustering Structures
Chao Gao, Yu Lu, Zongming Ma, Harrison H. Zhou; 17(161):1−29, 2016.
[abs][pdf][bib]

The Teaching Dimension of Linear Learners
Ji Liu, Xiaojin Zhu; 17(162):1−25, 2016.
[abs][pdf][bib]

Augmentable Gamma Belief Networks
Mingyuan Zhou, Yulai Cong, Bo Chen; 17(163):1−44, 2016.
[abs][pdf][bib]

Optimal Estimation of Derivatives in Nonparametric Regression
Wenlin Dai, Tiejun Tong, Marc G. Genton; 17(164):1−25, 2016.
[abs][pdf][bib]

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
Nihar B. Shah, Dengyong Zhou; 17(165):1−52, 2016.
[abs][pdf][bib]

Joint Structural Estimation of Multiple Graphical Models
Jing Ma, George Michailidis; 17(166):1−48, 2016.
[abs][pdf][bib]

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes
Yuanjia Wang, Tianle Chen, Donglin Zeng; 17(167):1−37, 2016.
[abs][pdf][bib]

Stable Graphical Models
Navodit Misra, Ercan E. Kuruoglu; 17(168):1−36, 2016.
[abs][pdf][bib]

Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach
Stavros P. Adam, George D. Magoulas, Dimitrios A. Karras, Michael N. Vrahatis; 17(169):1−40, 2016.
[abs][pdf][bib]

mlr: Machine Learning in R
Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones; 17(170):1−5, 2016.
[abs][pdf][bib]    [code][github]

Feature-Level Domain Adaptation
Wouter M. Kouw, Laurens J.P. van der Maaten, Jesse H. Krijthe, Marco Loog; 17(171):1−32, 2016.
[abs][pdf][bib]

Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues
David Rohde, Matt P. Wand; 17(172):1−47, 2016.
[abs][pdf][bib]

Online PCA with Optimal Regret
Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth; 17(173):1−49, 2016.
[abs][pdf][bib]

Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
Chiwoo Park, Jianhua Z. Huang; 17(174):1−29, 2016.
[abs][pdf][bib]

bandicoot: a Python Toolbox for Mobile Phone Metadata
Yves-Alexandre de Montjoye, Luc Rocher, Alex Sandy Pentland; 17(175):1−5, 2016.
[abs][pdf][bib]    [code][mit.edu]

Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels
Céline Brouard, Marie Szafranski, Florence d'Alché-Buc; 17(176):1−48, 2016.
[abs][pdf][bib]

A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries
Radoslaw Adamczak; 17(177):1−18, 2016.
[abs][pdf][bib]

The Asymptotic Performance of Linear Echo State Neural Networks
Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali; 17(178):1−35, 2016.
[abs][pdf][bib]

On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Vince Lyzinski, Keith Levin, Donniell E. Fishkind, Carey E. Priebe; 17(179):1−34, 2016.
[abs][pdf][bib]

Characteristic Kernels and Infinitely Divisible Distributions
Yu Nishiyama, Kenji Fukumizu; 17(180):1−28, 2016.
[abs][pdf][bib]

Consistency of Cheeger and Ratio Graph Cuts
Nicolás García Trillos, Dejan Slepčev, James von Brecht, Thomas Laurent, Xavier Bresson; 17(181):1−46, 2016.
[abs][pdf][bib]

Jointly Informative Feature Selection Made Tractable by Gaussian Modeling
Leonidas Lefakis, François Fleuret; 17(182):1−39, 2016.
[abs][pdf][bib]

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg; 17(183):1−40, 2016.
[abs][pdf][bib]

fastFM: A Library for Factorization Machines
Immanuel Bayer; 17(184):1−5, 2016.
[abs][pdf][bib]    [code][github]

The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes
Ramin Moghaddass, Cynthia Rudin, David Madigan; 17(185):1−24, 2016.
[abs][pdf][bib]

Electronic Health Record Analysis via Deep Poisson Factor Models
Ricardo Henao, James T. Lu, Joseph E. Lucas, Jeffrey Ferranti, Lawrence Carin; 17(186):1−32, 2016.
[abs][pdf][bib]

Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis
Zhirong Yang, Jukka Corander, Erkki Oja; 17(187):1−25, 2016.
[abs][pdf][bib]

A New Algorithm and Theory for Penalized Regression-based Clustering
Chong Wu, Sunghoon Kwon, Xiaotong Shen, Wei Pan; 17(188):1−25, 2016.
[abs][pdf][bib]

Classification of Imbalanced Data with a Geometric Digraph Family
Artür Manukyan, Elvan Ceyhan; 17(189):1−40, 2016.
[abs][pdf][bib]

A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes
Tobias Sutter, Arnab Ganguly, Heinz Koeppl; 17(190):1−37, 2016.
[abs][pdf][bib]

One-class classification of point patterns of extremes
Stijn Luca, David A. Clifton, Bart Vanrumste; 17(191):1−21, 2016.
[abs][pdf][bib]

On the Influence of Momentum Acceleration on Online Learning
Kun Yuan, Bicheng Ying, Ali H. Sayed; 17(192):1−66, 2016.
[abs][pdf][bib]

Data-driven Rank Breaking for Efficient Rank Aggregation
Ashish Khetan, Sewoong Oh; 17(193):1−54, 2016.
[abs][pdf][bib]

Optimal Learning Rates for Localized SVMs
Mona Meister, Ingo Steinwart; 17(194):1−44, 2016.
[abs][pdf][bib]

Bipartite Ranking: a Risk-Theoretic Perspective
Aditya Krishna Menon, Robert C. Williamson; 17(195):1−102, 2016.
[abs][pdf][bib]

Bayesian group factor analysis with structured sparsity
Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E Engelhardt; 17(196):1−47, 2016.
[abs][pdf][bib]

Machine Learning in an Auction Environment
Patrick Hummel, R. Preston McAfee; 17(197):1−37, 2016.
[abs][pdf][bib]

Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications
Oren Elisha, Shai Dekel; 17(198):1−38, 2016.
[abs][pdf][bib]

Mutual Information Based Matching for Causal Inference with Observational Data
Lei Sun, Alexander G. Nikolaev; 17(199):1−31, 2016.
[abs][pdf][bib]

Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles
Xiangju Qin, Pádraig Cunningham, Michael Salter-Townshend; 17(200):1−51, 2016.
[abs][pdf][bib]

Multivariate Spearman's rho for Aggregating Ranks Using Copulas
Justin Bedő, Cheng Soon Ong; 17(201):1−30, 2016.
[abs][pdf][bib]

Nonparametric Network Models for Link Prediction
Sinead A. Williamson; 17(202):1−21, 2016.
[abs][pdf][bib]

Guarding against Spurious Discoveries in High Dimensions
Jianqing Fan, Wen-Xin Zhou; 17(203):1−34, 2016.
[abs][pdf][bib]

Bayesian Graphical Models for Multivariate Functional Data
Hongxiao Zhu, Nate Strawn, David B. Dunson; 17(204):1−27, 2016.
[abs][pdf][bib]

Neural Autoregressive Distribution Estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle; 17(205):1−37, 2016.
[abs][pdf][bib]

ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
Ery Arias-Castro, David Mason, Bruno Pelletier; 17(206):1−4, 2016.
[abs][pdf][bib]

Modelling Interactions in High-dimensional Data with Backtracking
Rajen D. Shah; 17(207):1−31, 2016.
[abs][pdf][bib]

Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation
Sylvain Arlot, Matthieu Lerasle; 17(208):1−50, 2016.
[abs][pdf][bib]    [appendix]

Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
Shusen Wang, Zhihua Zhang, Tong Zhang; 17(210):1−49, 2016.
[abs][pdf][bib]

Multi-Objective Markov Decision Processes for Data-Driven Decision Support
Daniel J. Lizotte, Eric B. Laber; 17(211):1−28, 2016.
[abs][pdf][bib]

Measuring Dependence Powerfully and Equitably
Yakir A. Reshef, David N. Reshef, Hilary K. Finucane, Pardis C. Sabeti, Michael Mitzenmacher; 17(212):1−63, 2016.
[abs][pdf][bib]

Neyman-Pearson Classification under High-Dimensional Settings
Anqi Zhao, Yang Feng, Lie Wang, Xin Tong; 17(213):1−39, 2016.
[abs][pdf][bib]

A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
Garvesh Raskutti, Michael W. Mahoney; 17(214):1−31, 2016.
[abs][pdf][bib]

Learning Planar Ising Models
Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli; 17(215):1−26, 2016.
[abs][pdf][bib]

Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma
Murat A. Erdogdu; 17(216):1−52, 2016.
[abs][pdf][bib]

Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Xi Chen, Kevin Jiao, Qihang Lin; 17(217):1−40, 2016.
[abs][pdf][bib]

Multi-scale Classification using Localized Spatial Depth
Subhajit Dutta, Soham Sarkar, Anil K. Ghosh; 17(218):1−30, 2016.
[abs][pdf][bib]

On Bayes Risk Lower Bounds
Xi Chen, Adityanand Guntuboyina, Yuchen Zhang; 17(219):1−58, 2016.
[abs][pdf][bib]

Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize
Huizhen Yu; 17(220):1−58, 2016.
[abs][pdf][bib]

RLScore: Regularized Least-Squares Learners
Tapio Pahikkala, Antti Airola; 17(221):1−5, 2016.
[abs][pdf][bib]    [code][cs.utu.fi]

Stability and Generalization in Structured Prediction
Ben London, Bert Huang, Lise Getoor; 17(222):1−52, 2016.
[abs][pdf][bib]

Composite Multiclass Losses
Robert C. Williamson, Elodie Vernet, Mark D. Reid; 17(223):1−52, 2016.
[abs][pdf][bib]

Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview
Nuaman Asbeh, Boaz Lerner; 17(224):1−52, 2016.
[abs][pdf][bib]     [Part II]

GenSVM: A Generalized Multiclass Support Vector Machine
Gerrit J.J. van den Burg, Patrick J.F. Groenen; 17(225):1−42, 2016.
[abs][pdf][bib]     [code][github]

Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling
Terrance D. Savitsky; 17(226):1−49, 2016.
[abs][pdf][bib]

Approximate Newton Methods for Policy Search in Markov Decision Processes
Thomas Furmston, Guy Lever, David Barber; 17(227):1−51, 2016.
[abs][pdf][bib]

Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case
Joon Kwon, Vianney Perchet; 17(229):1−32, 2016.
[abs][pdf][bib]

Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption
Chenxin Ma, Rachael Tappenden, Martin Takáč; 17(230):1−24, 2016.
[abs][pdf][bib]

A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
Michael Chichignoud, Johannes Lederer, Martin J. Wainwright; 17(231):1−20, 2016.
[abs][pdf][bib]

A Characterization of Linkage-Based Hierarchical Clustering
Margareta Ackerman, Shai Ben-David; 17(232):1−17, 2016.
[abs][pdf][bib]

Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation
Nuaman Asbeh, Boaz Lerner; 17(233):1−45, 2016.
[abs][pdf][bib]     [Part I]

Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses
Peter Schulam, Suchi Saria; 17(234):1−35, 2016.
[abs][pdf][bib]

An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension
Bo Peng, Lan Wang, Yichao Wu; 17(236):1−26, 2016.
[abs][pdf][bib]

Blending Learning and Inference in Conditional Random Fields
Tamir Hazan, Alexander G. Schwing, Raquel Urtasun; 17(237):1−25, 2016.
[abs][pdf][bib]

Distributed Submodular Maximization
Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause; 17(238):1−44, 2016.
[abs][pdf][bib]

On the properties of variational approximations of Gibbs posteriors
Pierre Alquier, James Ridgway, Nicolas Chopin; 17(239):1−41, 2016.
[abs][pdf][bib]




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