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.
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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.
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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.
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- LIBOL: A Library for Online Learning Algorithms
- Steven C.H. Hoi, Jialei Wang, Peilin Zhao; (15):495−499, 2014.
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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.
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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.
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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.
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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.
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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.
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- Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos
- Camille Couprie, Cl\'{e}ment 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.
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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.
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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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