JMLR Volume 15

Bridging Viterbi and Posterior Decoding: A Generalized Risk Approach to Hidden Path Inference Based on Hidden Markov Models: Jüri Lember, Alexey A. Koloydenko; (1):1−58, 2014.
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Fast SVM Training Using Approximate Extreme Points: Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi; (2):59−98, 2014.
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Detecting Click Fraud in Online Advertising: A Data Mining Approach: Richard Oentaryo, Ee-Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Minh Nhut Nguyen, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar; (3):99−140, 2014.
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EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines: Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; (4):141−145, 2014. (Machine Learning Open Source Software Paper)
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A Junction Tree Framework for Undirected Graphical Model Selection: Divyanshu Vats, Robert D. Nowak; (5):147−191, 2014.
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Axioms for Graph Clustering Quality Functions: Twan van Laarhoven, Elena Marchiori; (6):193−215, 2014.
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Convex vs Non-Convex Estimators for Regression and Sparse Estimation: the Mean Squared Error Properties of ARD and GLasso: Aleksandr Aravkin, James V. Burke, Alessandro Chiuso, Gianluigi Pillonetto; (7):217−252, 2014.
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Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning: Aaron Wilson, Alan Fern, Prasad Tadepalli; (8):253−282, 2014.
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Information Theoretical Estimators Toolbox: Zoltán Szabó; (9):283−287, 2014. (Machine Learning Open Source Software Paper)
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Off-policy Learning With Eligibility Traces: A Survey: Matthieu Geist, Bruno Scherrer; (10):289−333, 2014.
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Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule: Garvesh Raskutti, Martin J. Wainwright, Bin Yu; (11):335−366, 2014.
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Unbiased Generative Semi-Supervised Learning: Patrick Fox-Roberts, Edward Rosten; (12):367−443, 2014.
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Node-Based Learning of Multiple Gaussian Graphical Models: Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee; (13):445−488, 2014.
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The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R: Haotian Pang, Han Liu, Robert V, erbei; (14):489−493, 2014. (Machine Learning Open Source Software Paper)
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LIBOL: A Library for Online Learning Algorithms: Steven C.H. Hoi, Jialei Wang, Peilin Zhao; (15):495−499, 2014. (Machine Learning Open Source Software Paper)
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Improving Markov Network Structure Learning Using Decision Trees: Daniel Lowd, Jesse Davis; (16):501−532, 2014.
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Ground Metric Learning: Marco Cuturi, David Avis; (17):533−564, 2014.
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Link Prediction in Graphs with Autoregressive Features: Emile Richard, Stéphane Gaïffas, Nicolas Vayatis; (18):565−593, 2014.
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Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression: Francis Bach; (19):595−627, 2014.
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Random Intersection Trees: Rajen Dinesh Shah, Nicolai Meinshausen; (20):629−654, 2014.
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Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers: Brett L Moore, Larry D Pyeatt, Vivekan, Kulkarni, Periklis Panousis, Kevin Padrez, Anthony G Doufas; (21):655−696, 2014.
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Clustering Hidden Markov Models with Variational HEM: Emanuele Coviello, Antoni B. Chan, Gert R.G. Lanckriet; (22):697−747, 2014.
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A Novel M-Estimator for Robust PCA: Teng Zhang, Gilad Lerman; (23):749−808, 2014.
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Policy Evaluation with Temporal Differences: A Survey and Comparison: Christoph Dann, Gerhard Neumann, Jan Peters; (24):809−883, 2014.
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Active Learning Using Smooth Relative Regret Approximations with Applications: Nir Ailon, Ron Begleiter, Esther Ezra; (25):885−920, 2014.
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An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation: Henning Sprekeler, Tiziano Zito, Laurenz Wiskott; (26):921−947, 2014.
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Natural Evolution Strategies: Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, J\"{u}rgen Schmidhuber; (27):949−980, 2014.
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Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation: Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; (28):981−1009, 2014. (Machine Learning Open Source Software Paper)
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Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability: Tomohiko Mizutani; (29):1011−1039, 2014.
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Improving Prediction from Dirichlet Process Mixtures via Enrichment: Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa; (30):1041−1071, 2014.
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Gibbs Max-margin Topic Models with Data Augmentation: Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang; (31):1073−1110, 2014.
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A Reliable Effective Terascale Linear Learning System: Alekh Agarwal, Oliveier Chapelle, Miroslav Dud\'{i}k, John Langford; (32):1111−1133, 2014.
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New Learning Methods for Supervised and Unsupervised Preference Aggregation: Maksims N. Volkovs, Richard S. Zemel; (33):1135−1176, 2014.
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Prediction and Clustering in Signed Networks: A Local to Global Perspective: Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari; (34):1177−1213, 2014.
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Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders: Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fern, o Perez-Cruz; (35):1215−1247, 2014.
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Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization: Nicolas Gillis, Robert Luce; (36):1249−1280, 2014.
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Follow the Leader If You Can, Hedge If You Must: Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen; (37):1281−1316, 2014.
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Structured Prediction via Output Space Search: Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli; (38):1317−1350, 2014.
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Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing: Matt P. W, ; (39):1351−1369, 2014.
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Towards Ultrahigh Dimensional Feature Selection for Big Data: Mingkui Tan, Ivor W. Tsang, Li Wang; (40):1371−1429, 2014.
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Adaptive Sampling for Large Scale Boosting: Charles Dubout, Francois Fleuret; (41):1431−1453, 2014.
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Manopt, a Matlab Toolbox for Optimization on Manifolds: Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; (42):1455−1459, 2014. (Machine Learning Open Source Software Paper)
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Training Highly Multiclass Classifiers: Maya R. Gupta, Samy Bengio, Jason Weston; (43):1461−1492, 2014.
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Locally Adaptive Factor Processes for Multivariate Time Series: Daniele Durante, Bruno Scarpa, David B. Dunson; (44):1493−1522, 2014.
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Iteration Complexity of Feasible Descent Methods for Convex Optimization: Po-Wei Wang, Chih-Jen Lin; (45):1523−1548, 2014.
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High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models: Majid Janzamin, Animashree Anandkumar; (46):1549−1591, 2014.
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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo: Matthew D. Hoffman, Andrew Gelman; (47):1593−1623, 2014.
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Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife: Stefan Wager, Trevor Hastie, Bradley Efron; (48):1625−1651, 2014.
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Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses: Shivani Agarwal; (49):1653−1674, 2014.
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Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls: Zhan Wang, Sandra Paterlini, Fuchang Gao, Yuhong Yang; (50):1675−1711, 2014.
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Graph Estimation From Multi-Attribute Data: Mladen Kolar, Han Liu, Eric P. Xing; (51):1713−1750, 2014.
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Hitting and Commute Times in Large Random Neighborhood Graphs: Ulrike von Luxburg, Agnes Radl, Matthias Hein; (52):1751−1798, 2014.
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Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs: Jun Zhu, Ning Chen, Eric P. Xing; (53):1799−1847, 2014.
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Expectation Propagation for Neural Networks with Sparsity-Promoting Priors: Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari; (54):1849−1901, 2014.
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Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems: Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann; (55):1903−1928, 2014.
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting: Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov; (56):1929−1958, 2014.
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Sparse Factor Analysis for Learning and Content Analytics: Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk; (57):1959−2008, 2014.
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Causal Discovery with Continuous Additive Noise Models: Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf; (58):2009−2053, 2014.
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pystruct - Learning Structured Prediction in Python: Andreas C. Müller, Sven Behnke; (59):2055−2060, 2014. (Machine Learning Open Source Software Paper)
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The Student-t Mixture as a Natural Image Patch Prior with Application to Image Compression: A{\"a}ron van den Oord, Benjamin Schrauwen; (60):2061−2086, 2014.
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Parallel MCMC with Generalized Elliptical Slice Sampling: Robert Nishihara, Iain Murray, Ryan P. Adams; (61):2087−2112, 2014.
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Classifier Cascades and Trees for Minimizing Feature Evaluation Cost: Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle; (62):2113−2144, 2014.
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Particle Gibbs with Ancestor Sampling: Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön; (63):2145−2184, 2014.
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Ramp Loss Linear Programming Support Vector Machine: Xiaolin Huang, Lei Shi, Johan A.K. Suykens; (64):2185−2211, 2014.
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Clustering Partially Observed Graphs via Convex Optimization: Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu; (65):2213−2238, 2014.
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A Tensor Approach to Learning Mixed Membership Community Models: Animashree An, kumar, Rong Ge, Daniel Hsu, Sham M. Kakade; (66):2239−2312, 2014.
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Cover Tree Bayesian Reinforcement Learning: Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas; (67):2313−2335, 2014.
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Efficient State-Space Inference of Periodic Latent Force Models: Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings; (68):2337−2397, 2014.
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Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity: Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, Lyle Ungar; (69):2399−2449, 2014.
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On Multilabel Classification and Ranking with Bandit Feedback: Claudio Gentile, Francesco Orabona; (70):2451−2487, 2014.
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Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization: Elad Hazan, Satyen Kale; (71):2489−2512, 2014.
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One-Shot-Learning Gesture Recognition using HOG-HOF Features: Jakub Konecny, Michal Hagara; (72):2513−2532, 2014.
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Contextual Bandits with Similarity Information: Aleks, rs Slivkins; (73):2533−2568, 2014.
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Boosting Algorithms for Detector Cascade Learning: Mohammad Saberian, Nuno Vasconcelos; (74):2569−2605, 2014.
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Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions: Amit Dhur, har, Marek Petrik; (75):2607−2627, 2014.
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Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions: Shohei Shimizu, Kenneth Bollen; (76):2629−2652, 2014.
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A Truncated EM Approach for Spike-and-Slab Sparse Coding: Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke; (77):2653−2687, 2014.
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Efficient Occlusive Components Analysis: Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke; (78):2689−2722, 2014.
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Optimality of Graphlet Screening in High Dimensional Variable Selection: Jiashun Jin, Cun-Hui Zhang, Qi Zhang; (79):2723−2772, 2014.
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Tensor Decompositions for Learning Latent Variable Models: Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky; (80):2773−2832, 2014.
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Bayesian Entropy Estimation for Countable Discrete Distributions: Evan Archer, Il Memming Park, Jonathan W. Pillow; (81):2833−2868, 2014.
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Confidence Intervals and Hypothesis Testing for High-Dimensional Regression: Adel Javanmard, Andrea Montanari; (82):2869−2909, 2014.
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QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation: Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar; (83):2911−2947, 2014.
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Multimodal Learning with Deep Boltzmann Machines: Nitish Srivastava, Ruslan Salakhutdinov; (84):2949−2980, 2014.
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Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs: Braxton Osting, Christoph Brune, Stanley J. Osher; (85):2981−3012, 2014.
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Bayesian Co-Boosting for Multi-modal Gesture Recognition: Jiaxiang Wu, Jian Cheng; (86):3013−3036, 2014.
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Effective String Processing and Matching for Author Disambiguation: Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin; (87):3037−3064, 2014.
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High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation: Po-Ling Loh, Peter Bühlmann; (88):3065−3105, 2014.
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Recursive Teaching Dimension, VC-Dimension and Sample Compression: Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, Sandra Zilles; (89):3107−3131, 2014.
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Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?: Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim; (90):3133−3181, 2014.
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ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation: Ivo Couckuyt, Tom Dhaene, Piet Demeester; (91):3183−3186, 2014. (Machine Learning Open Source Software Paper)
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Robust Online Gesture Recognition with Crowdsourced Annotations: Long-Van Nguyen-Dinh, Alberto Calatroni, Gerhard Tr\"{o}ster; (92):3187−3220, 2014.
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Accelerating t-SNE using Tree-Based Algorithms: Laurens van der Maaten; (93):3221−3245, 2014.
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Set-Valued Approachability and Online Learning with Partial Monitoring: Shie Mannor, Vianney Perchet, Gilles Stoltz; (94):3247−3295, 2014.
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Learning Graphical Models With Hubs: Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten; (95):3297−3331, 2014.
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Inconsistency of Pitman-Yor Process Mixtures for the Number of Components: Jeffrey W. Miller, Matthew T. Harrison; (96):3333−3370, 2014.
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Active Contextual Policy Search: Alexander Fabisch, Jan Hendrik Metzen; (97):3371−3399, 2014.
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Matrix Completion with the Trace Norm: Learning, Bounding, and Transducing: Ohad Shamir, Shai Shalev-Shwartz; (98):3401−3423, 2014.
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Statistical Analysis of Metric Graph Reconstruction: Fabrizio Lecci, Aless, ro Rinaldo, Larry Wasserman; (99):3425−3446, 2014.
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Alternating Linearization for Structured Regularization Problems: Xiaodong Lin, Minh Pham, Andrzej Ruszczy\'{n}ski; (100):3447−3481, 2014.
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The Gesture Recognition Toolkit: Nicholas Gillian, Joseph A. Paradiso; (101):3483−3487, 2014. (Machine Learning Open Source Software Paper)
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Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos: Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun; (102):3489−3511, 2014.
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On the Bayes-Optimality of F-Measure Maximizers: Willem Waegeman, Krzysztof Dembczy{\'n}ski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier; (103):3513−3568, 2014.
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SPMF: A Java Open-Source Pattern Mining Library: Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; (104):3569−3573, 2014. (Machine Learning Open Source Software Paper)
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Efficient Learning and Planning with Compressed Predictive States: William Hamilton, Mahdi Milani Fard, Joelle Pineau; (105):3575−3619, 2014.
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Revisiting Stein's Paradox: Multi-Task Averaging: Sergey Feldman, Maya R. Gupta, Bela A. Frigyik; (106):3621−3662, 2014.
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Multi-Objective Reinforcement Learning using Sets of Pareto Dominating Policies: Kristof Van Moffaert, Ann Nowé; (107):3663−3692, 2014.
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Seeded Graph Matching for Correlated Erdos-Renyi Graphs: Vince Lyzinski, Donniell E. Fishkind, Carey E. Priebe; (108):3693−3720, 2014.
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Asymptotic Accuracy of Distribution-Based Estimation of Latent Variables: Keisuke Yamazaki; (109):3721−3742, 2014.
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What Regularized Auto-Encoders Learn from the Data-Generating Distribution: Guillaume Alain, Yoshua Bengio; (110):3743−3773, 2014.
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Revisiting Bayesian Blind Deconvolution: David Wipf, Haichao Zhang; (111):3775−3814, 2014.
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New Results for Random Walk Learning: Jeffrey C. Jackson, Karl Wimmer; (112):3815−3846, 2014.
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Transfer Learning Decision Forests for Gesture Recognition: Norberto A. Goussies, Sebastián Ubalde, Marta Mejail; (113):3847−3870, 2014.
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Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning: Toke J. Hansen, Michael W. Mahoney; (114):3871−3914, 2014.
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BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits: Ruben Martinez-Cantin; (115):3915−3919, 2014. (Machine Learning Open Source Software Paper)
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Order-Independent Constraint-Based Causal Structure Learning: Diego Colombo, Marloes H. Maathuis; (116):3921−3962, 2014.
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Effective Sampling and Learning for Mallows Models with Pairwise-Preference Data: Tyler Lu, Craig Boutilier; (117):3963−4009, 2014.
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Robust Hierarchical Clustering: Maria-Florina Balcan, Yingyu Liang, Pramod Gupta; (118):4011−4051, 2014.
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Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization: Thomas Desautels, Andreas Krause, Joel W. Burdick; (119):4053−4103, 2014.
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Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning: Kshitij Judah, Alan P. Fern, Thomas G. Dietterich, Prasad Tadepalli; (120):4105−4143, 2014.
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