JMLR Volume 16

Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling: Xi Chen, Qihang Lin, Dengyong Zhou; (1):1−46, 2015.
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Simultaneous Pursuit of Sparseness and Rank Structures for Matrix Decomposition: Qi Yan, Jieping Ye, Xiaotong Shen; (2):47−75, 2015.
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Statistical Topological Data Analysis using Persistence Landscapes: Peter Bubenik; (3):77−102, 2015.
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Links Between Multiplicity Automata, Observable Operator Models and Predictive State Representations -- a Unified Learning Framework: Michael Thon, Herbert Jaeger; (4):103−147, 2015.
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SAMOA: Scalable Advanced Massive Online Analysis: Gianmarco De Francisci Morales, Albert Bifet; (5):149−153, 2015. (Machine Learning Open Source Software Paper)
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Online Learning via Sequential Complexities: Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari; (6):155−186, 2015.
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Learning Transformations for Clustering and Classification: Qiang Qiu, Guillermo Sapiro; (7):187−225, 2015.
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Multi-layered Gesture Recognition with Kinect: Feng Jiang, Shengping Zhang, Shen Wu, Yang Gao, Debin Zhao; (8):227−254, 2015.
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Multimodal Gesture Recognition via Multiple Hypotheses Rescoring: Vassilis Pitsikalis, Athanasios Katsamanis, Stavros Theodorakis, Petros Maragos; (9):255−284, 2015.
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An Asynchronous Parallel Stochastic Coordinate Descent Algorithm: Ji Liu, Stephen J. Wright, Christopher Ré, Victor Bittorf, Srikrishna Sridhar; (10):285−322, 2015.
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Geometric Intuition and Algorithms for Ev--SVM: Álvaro Barbero, Akiko Takeda, Jorge López; (11):323−369, 2015.
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Composite Self-Concordant Minimization: Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher; (12):371−416, 2015.
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Network Granger Causality with Inherent Grouping Structure: Sumanta Basu, Ali Shojaie, George Michailidis; (13):417−453, 2015.
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Iterative and Active Graph Clustering Using Trace Norm Minimization Without Cluster Size Constraints: Nir Ailon, Yudong Chen, Huan Xu; (14):455−490, 2015.
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A Classification Module for Genetic Programming Algorithms in JCLEC: Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; (15):491−494, 2015. (Machine Learning Open Source Software Paper)
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AD3: Alternating Directions Dual Decomposition for MAP Inference in Graphical Models: André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing; (16):495−545, 2015.
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Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit: Felix Weninger; (17):547−551, 2015. (Machine Learning Open Source Software Paper)
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The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R: Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; (18):553−557, 2015. (Machine Learning Open Source Software Paper)
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Regularized M-estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima: Po-Ling Loh, Martin J. Wainwright; (19):559−616, 2015.
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Generalized Hierarchical Kernel Learning: Pratik Jawanpuria, Jagarlapudi Saketha Nath, Ganesh Ramakrishnan; (20):617−652, 2015.
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Discrete Restricted Boltzmann Machines: Guido Montúfar, Jason Morton; (21):653−672, 2015.
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Evolving GPU Machine Code: Cleomar Pereira da Silva, Douglas Mota Dias, Cristiana Bentes, Marco Aur\'{e}lio Cavalcanti Pacheco, Le, ro Fontoura Cupertino; (22):673−712, 2015.
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A Compression Technique for Analyzing Disagreement-Based Active Learning: Yair Wiener, Steve Hanneke, Ran El-Yaniv; (23):713−745, 2015.
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Response-Based Approachability with Applications to Generalized No-Regret Problems: Andrey Bernstein, Nahum Shimkin; (24):747−773, 2015.
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Strong Consistency of the Prototype Based Clustering in Probabilistic Space: Vladimir Nikulin; (25):775−785, 2015.
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Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm: Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario March, Jean-Francis Roy; (26):787−860, 2015.
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A Statistical Perspective on Algorithmic Leveraging: Ping Ma, Michael W. Mahoney, Bin Yu; (27):861−911, 2015.
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Distributed Matrix Completion and Robust Factorization: Lester Mackey, Ameet Talwalkar, Michael I. Jordan; (28):913−960, 2015.
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Combined l1 and Greedy l0 Penalized Least Squares for Linear Model Selection: Piotr Pokarowski, Jan Mielniczuk; (29):961−992, 2015.
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Learning with the Maximum Correntropy Criterion Induced Losses for Regression: Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, Johan A.K. Suykens; (30):993−1034, 2015.
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Joint Estimation of Multiple Precision Matrices with Common Structures: Wonyul Lee, Yufeng Liu; (31):1035−1062, 2015.
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Lasso Screening Rules via Dual Polytope Projection: Jie Wang, Peter Wonka, Jieping Ye; (32):1063−1101, 2015.
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Fast Cross-Validation via Sequential Testing: Tammo Krueger, Danny Panknin, Mikio Braun; (33):1103−1155, 2015.
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Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data: Jean Honorio, Luis Ortiz; (34):1157−1210, 2015.
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Local Identification of Overcomplete Dictionaries: Karin Schnass; (35):1211−1242, 2015.
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Encog: Library of Interchangeable Machine Learning Models for Java and C#: Jeff Heaton; (36):1243−1247, 2015. (Machine Learning Open Source Software Paper)
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Perturbed Message Passing for Constraint Satisfaction Problems: Siamak Ravanbakhsh, Russell Greiner; (37):1249−1274, 2015.
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Learning Sparse Low-Threshold Linear Classifiers: Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang; (38):1275−1304, 2015.
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Learning Equilibria of Games via Payoff Queries: John Fearnley, Martin Gairing, Paul W. Goldberg, Rahul Savani; (39):1305−1344, 2015.
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Rationality, Optimism and Guarantees in General Reinforcement Learning: Peter Sunehag, Marcus Hutter; (40):1345−1390, 2015.
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The Algebraic Combinatorial Approach for Low-Rank Matrix Completion: Franz J.Király, Louis Theran, Ryota Tomioka; (41):1391−1436, 2015.
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A Comprehensive Survey on Safe Reinforcement Learning: Javier García, Fern, o Fernández; (42):1437−1480, 2015.
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Second-Order Non-Stationary Online Learning for Regression: Edward Moroshko, Nina Vaits, Koby Crammer; (43):1481−1517, 2015.
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A Finite Sample Analysis of the Naive Bayes Classifier: Daniel Berend, Aryeh Kontorovich; (44):1519−1545, 2015.
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Flexible High-Dimensional Classification Machines and Their Asymptotic Properties: Xingye Qiao, Lingsong Zhang; (45):1547−1572, 2015.
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RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research: Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How; (46):1573−1578, 2015. (Machine Learning Open Source Software Paper)
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Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery: Han Liu, Lie Wang, Tuo Zhao; (47):1579−1606, 2015.
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Bayesian Nonparametric Crowdsourcing: Pablo G. Moreno, Antonio Artes-Rodriguez, Yee Whye Teh, Fern, o Perez-Cruz; (48):1607−1627, 2015.
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Approximate Modified Policy Iteration and its Application to the Game of Tetris: Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon, Boris Lesner, Matthieu Geist; (49):1629−1676, 2015.
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Preface to this Special Issue: Alex Gammerman, Vladimir Vovk; (50):1677−1681, 2015.
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V-Matrix Method of Solving Statistical Inference Problems: Vladimir Vapnik, Rauf Izmailov; (51):1683−1730, 2015.
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Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization: Adith Swaminathan, Thorsten Joachims; (52):1731−1755, 2015.
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Optimal Estimation of Low Rank Density Matrices: Vladimir Koltchinskii, Dong Xia; (53):1757−1792, 2015.
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Fast Rates in Statistical and Online Learning: Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson; (54):1793−1861, 2015.
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On the Asymptotic Normality of an Estimate of a Regression Functional: László Györfi, Harro Walk; (55):1863−1877, 2015.
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Sharp Oracle Bounds for Monotone and Convex Regression Through Aggregation: Pierre C. Bellec, Alexandre B. Tsybakov; (56):1879−1892, 2015.
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Exceptional Rotations of Random Graphs: A VC Theory: Louigi Addario-Berry, Shankar Bhamidi, Sébastien Bubeck, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira; (57):1893−1922, 2015.
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Semi-Supervised Interpolation in an Anticausal Learning Scenario: Dominik Janzing, Bernhard Schölkopf; (58):1923−1948, 2015.
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Towards an Axiomatic Approach to Hierarchical Clustering of Measures: Philipp Thomann, Ingo Steinwart, Nico Schmid; (59):1949−2002, 2015.
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Predicting a Switching Sequence of Graph Labelings: Mark Herbster, Stephen Pasteris, Massimiliano Pontil; (60):2003−2022, 2015.
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Learning Using Privileged Information: Similarity Control and Knowledge Transfer: Vladimir Vapnik, Rauf Izmailov; (61):2023−2049, 2015.
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Alexey Chervonenkis's Bibliography: Introductory Comments: Alex Gammerman, Vladimir Vovk; (62):2051−2066, 2015.
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Alexey Chervonenkis's Bibliography: Alex Gammerman, Vladimir Vovk; (63):2067−2080, 2015.
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Photonic Delay Systems as Machine Learning Implementations: Michiel Hermans, Miguel C. Soriano, Joni Dambre, Peter Bienstman, Ingo Fischer; (64):2081−2097, 2015.
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On Linearly Constrained Minimum Variance Beamforming: Jian Zhang, Chao Liu; (65):2099−2145, 2015.
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Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets: Sofia Triantafillou, Ioannis Tsamardinos; (66):2147−2205, 2015.
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Existence and Uniqueness of Proper Scoring Rules: Evgeni Y. Ovcharov; (67):2207−2230, 2015.
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Adaptive Strategy for Stratified Monte Carlo Sampling: Alexandra Carpentier, Remi Munos, András Antos; (68):2231−2271, 2015.
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Concave Penalized Estimation of Sparse Gaussian Bayesian Networks: Bryon Aragam, Qing Zhou; (69):2273−2328, 2015.
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Agnostic Insurability of Model Classes: Narayana Santhanam, Venkat Anantharam; (70):2329−2355, 2015.
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Achievability of Asymptotic Minimax Regret by Horizon-Dependent and Horizon-Independent Strategies: Kazuho Watanabe, Teemu Roos; (71):2357−2375, 2015.
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Multiclass Learnability and the ERM Principle: Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz; (72):2377−2404, 2015.
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Geometry and Expressive Power of Conditional Restricted Boltzmann Machines: Guido Montúfar, Nihat Ay, Keyan Ghazi-Zahedi; (73):2405−2436, 2015.
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From Dependency to Causality: A Machine Learning Approach: Gianluca Bontempi, Maxime Flauder; (74):2437−2457, 2015.
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The Libra Toolkit for Probabilistic Models: Daniel Lowd, Amirmohammad Rooshenas; (75):2459−2463, 2015. (Machine Learning Open Source Software Paper)
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Complexity of Equivalence and Learning for Multiplicity Tree Automata: Ines Marusic, James Worrell; (76):2465−2500, 2015.
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Bayesian Nonparametric Covariance Regression: Emily B. Fox, David B. Dunson; (77):2501−2542, 2015.
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A General Framework for Fast Stagewise Algorithms: Ryan J. Tibshirani; (78):2543−2588, 2015.
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Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs: Yangbo He, Jinzhu Jia, Bin Yu; (79):2589−2609, 2015.
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pyGPs -- A Python Library for Gaussian Process Regression and Classification: Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; (80):2611−2616, 2015. (Machine Learning Open Source Software Paper)
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Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regression: WenWu Wang, Lu Lin; (81):2617−2641, 2015.
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When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity: Animashree An, kumar, Daniel Hsu, Majid Janzamin, Sham Kakade; (82):2643−2694, 2015.
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Absent Data Generating Classifier for Imbalanced Class Sizes: Arash Pourhabib, Bani K. Mallick, Yu Ding; (83):2695−2724, 2015.
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Decision Boundary for Discrete Bayesian Network Classifiers: Gherardo Var, o, Concha Bielza, Pedro Larranaga; (84):2725−2749, 2015.
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A View of Margin Losses as Regularizers of Probability Estimates: Hamed Masnadi-Shirazi, Nuno Vasconcelos; (85):2751−2795, 2015.
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Online Tensor Methods for Learning Latent Variable Models: Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree An, kumar; (86):2797−2835, 2015.
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Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior: Debdeep Pati, Anirban Bhattacharya, Guang Cheng; (87):2837−2851, 2015.
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CEKA: A Tool for Mining the Wisdom of Crowds: Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; (88):2853−2858, 2015. (Machine Learning Open Source Software Paper)
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Linear Dimensionality Reduction: Survey, Insights, and Generalizations: John P. Cunningham, Zoubin Ghahramani; (89):2859−2900, 2015.
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The Randomized Causation Coefficient: David Lopez-Paz, Krikamol Mu, et, Benjamin Recht; (90):2901−2907, 2015.
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Optimality of Poisson Processes Intensity Learning with Gaussian Processes: Alisa Kirichenko, Harry van Zanten; (91):2909−2919, 2015.
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Combination of Feature Engineering and Ranking Models for Paper-Author Identification in KDD Cup 2013: Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-de Lin, Hsuan-Tien Lin, Chih-Jen Lin; (92):2921−2947, 2015.
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Comparing Hard and Overlapping Clusterings: Danilo Horta, Ricardo J.G.B. Campello; (93):2949−2997, 2015.
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Completing Any Low-rank Matrix, Provably: Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward; (94):2999−3034, 2015.
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Eigenwords: Spectral Word Embeddings: Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar; (95):3035−3078, 2015.
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Discrete Reproducing Kernel Hilbert Spaces: Sampling and Distribution of Dirac-masses: Palle Jorgensen, Feng Tian; (96):3079−3114, 2015.
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A Direct Estimation of High Dimensional Stationary Vector Autoregressions: Fang Han, Huanran Lu, Han Liu; (97):3115−3150, 2015.
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Global Convergence of Online Limited Memory BFGS: Aryan Mokhtari, Alej, ro Ribeiro; (98):3151−3181, 2015.
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On Semi-Supervised Linear Regression in Covariate Shift Problems: Kenneth Joseph Ryan, Mark Vere Culp; (99):3183−3217, 2015.
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Ultra-Scalable and Efficient Methods for Hybrid Observational and Experimental Local Causal Pathway Discovery: Alexander Statnikov, Sisi Ma, Mikael Henaff, Nikita Lytkin, Efstratios Efstathiadis, Eric R. Peskin, Constantin F. Aliferis; (100):3219−3267, 2015.
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Plug-and-Play Dual-Tree Algorithm Runtime Analysis: Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram; (101):3269−3297, 2015.
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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates: Yuchen Zhang, John Duchi, Martin Wainwright; (102):3299−3340, 2015.
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Learning Theory of Randomized Kaczmarz Algorithm: Junhong Lin, Ding-Xuan Zhou; (103):3341−3365, 2015.
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Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares: Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh; (104):3367−3402, 2015.
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On the Inductive Bias of Dropout: David P. Helmbold, Philip M. Long; (105):3403−3454, 2015.
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Agnostic Learning of Disjunctions on Symmetric Distributions: Vitaly Feldman, Pravesh Kothari; (106):3455−3467, 2015.
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SnFFT: A Julia Toolkit for Fourier Analysis of Functions over Permutations: Gregory Plumb, Deepti Pachauri, Risi Kondor, Vikas Singh; (107):3469−3473, 2015.
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The Sample Complexity of Learning Linear Predictors with the Squared Loss: Ohad Shamir; (108):3475−3486, 2015.
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Minimax Analysis of Active Learning: Steve Hanneke, Liu Yang; (109):3487−3602, 2015.
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Convergence Rates for Persistence Diagram Estimation in Topological Data Analysis: Frédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel; (110):3603−3635, 2015.
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Supervised Learning via Euler's Elastica Models: Tong Lin, Hanlin Xue, Ling Wang, Bo Huang, Hongbin Zha; (111):3637−3686, 2015.
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Learning to Identify Concise Regular Expressions that Describe Email Campaigns: Paul Prasse, Christoph Sawade, Niels L, wehr, Tobias Scheffer; (112):3687−3720, 2015.
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Non-Asymptotic Analysis of a New Bandit Algorithm for Semi-Bounded Rewards: Junya Honda, Akimichi Takemura; (113):3721−3756, 2015.
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Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA: Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan; (114):3757−3811, 2015.
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Graphical Models via Univariate Exponential Family Distributions: Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu; (115):3813−3847, 2015.
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Marginalizing Stacked Linear Denoising Autoencoders: Minmin Chen, Kilian Q. Weinberger, Zhixiang (Eddie) Xu, Fei Sha; (116):3849−3875, 2015.
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PAC Optimal MDP Planning with Application to Invasive Species Management: Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, H. Jo Albers; (117):3877−3903, 2015.
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partykit: A Modular Toolkit for Recursive Partytioning in R: Torsten Hothorn, Achim Zeileis; (118):3905−3909, 2015. (Machine Learning Open Source Software Paper)
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