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JMLR Volume 14

Manifold Regularization and Semi-supervised Learning: Some Theoretical Analyses
Partha Niyogi; (1):1229−1250, 2013.
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Random Spanning Trees and the Prediction of Weighted Graphs
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella; (2):1251−1284, 2013.
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Regularization-Free Principal Curve Estimation
Samuel Gerber, Ross Whitaker; (3):1285−1302, 2013.
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Stochastic Variational Inference
Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley; (4):1303−1347, 2013.
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Multicategory Large-Margin Unified Machines
Chong Zhang, Yufeng Liu; (5):1349−1386, 2013.
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Finding Optimal Bayesian Networks Using Precedence Constraints
Pekka Parviainen, Mikko Koivisto; (6):1387−1415, 2013.
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JKernelMachines: A Simple Framework for Kernel Machines
David Picard, Nicolas Thome, Matthieu Cord; (7):1417−1421, 2013.
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Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators
Pierre Neuvial; (8):1423−1459, 2013.
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Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems
Takafumi Kanamori, Akiko Takeda, Taiji Suzuki; (9):1461−1504, 2013.
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A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar; (10):1505−1511, 2013.
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On the Learnability of Shuffle Ideals
Dana Angluin, James Aspnes, Sarah Eisenstat, Aryeh Kontorovich; (11):1513−1531, 2013.
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Fast Generalized Subset Scan for Anomalous Pattern Detection
Edward McFowl, III, Skyler Speakman, Daniel B. Neill; (12):1533−1561, 2013.
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Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion
Rami Mahdi, Jason Mezey; (13):1563−1603, 2013.
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Dimension Independent Similarity Computation
Reza Bosagh Zadeh, Ashish Goel; (14):1605−1626, 2013.
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Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos
Anastasios Roussos, Stavros Theodorakis, Vassilis Pitsikalis, Petros Maragos; (15):1627−1663, 2013.
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Nonparametric Sparsity and Regularization
Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Aless, ro Verri; (16):1665−1714, 2013.
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Similarity-based Clustering by Left-Stochastic Matrix Factorization
Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel; (17):1715−1746, 2013.
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On the Convergence of Maximum Variance Unfolding
Ery Arias-Castro, Bruno Pelletier; (18):1747−1770, 2013.
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Variable Selection in High-Dimension with Random Designs and Orthogonal Matching Pursuit
Antony Joseph; (19):1771−1800, 2013.
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Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs
Matthew J. Urry, Peter Sollich; (20):1801−1835, 2013.
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Distributions of Angles in Random Packing on Spheres
Tony Cai, Jianqing Fan, Tiefeng Jiang; (21):1837−1864, 2013.
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Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty
Wei Pan, Xiaotong Shen, Binghui Liu; (22):1865−1889, 2013.
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Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont; (23):1891−1945, 2013.
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Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting
Nayyar A. Zaidi, Jesús Cerquides, Mark J. Carman, Geoffrey I. Webb; (24):1947−1988, 2013.
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Machine Learning with Operational Costs
Theja Tulab, hula, Cynthia Rudin; (25):1989−2028, 2013.
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Approximating the Permanent with Fractional Belief Propagation
Michael Chertkov, Adam B. Yedidia; (26):2029−2066, 2013.
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Construction of Approximation Spaces for Reinforcement Learning
Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer; (27):2067−2118, 2013.
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Distribution-Dependent Sample Complexity of Large Margin Learning
Sivan Sabato, Nathan Srebro, Naftali Tishby; (28):2119−2149, 2013.
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Convex and Scalable Weakly Labeled SVMs
Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou; (29):2151−2188, 2013.
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Language-Motivated Approaches to Action Recognition
Manavender R. Malgireddy, Ifeoma Nwogu, Venu Govindaraju; (30):2189−2212, 2013.
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Segregating Event Streams and Noise with a Markov Renewal Process Model
Dan Stowell, Mark D. Plumbley; (31):2213−2238, 2013.
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Gaussian Kullback-Leibler Approximate Inference
Edward Challis, David Barber; (32):2239−2286, 2013.
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Message-Passing Algorithms for Quadratic Minimization
Nicholas Ruozzi, Sekhar Tatikonda; (33):2287−2314, 2013.
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The Rate of Convergence of AdaBoost
Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire; (34):2315−2347, 2013.
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Orange: Data Mining Toolbox in Python
Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, Blaž Zupan; (35):2349−2353, 2013.
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Tapkee: An Efficient Dimension Reduction Library
Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; (36):2355−2359, 2013.
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On the Mutual Nearest Neighbors Estimate in Regression
Arnaud Guyader, Nick Hengartner; (37):2361−2376, 2013.
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Distance Preserving Embeddings for General n-Dimensional Manifolds
Nakul Verma; (38):2415−2448, 2013.
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Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
Julien Mairal, Bin Yu; (39):2449−2485, 2013.
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Greedy Feature Selection for Subspace Clustering
Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk; (40):2487−2517, 2013.
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Learning Bilinear Model for Matching Queries and Documents
Wei Wu, Zhengdong Lu, Hang Li; (41):2519−2548, 2013.
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One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features
Jun Wan, Qiuqi Ruan, Wei Li, Shuang Deng; (42):2549−2582, 2013.
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Efficient Active Learning of Halfspaces: An Aggressive Approach
Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz; (43):2583−2615, 2013.
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Keep It Simple And Sparse: Real-Time Action Recognition
Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone; (44):2617−2640, 2013.
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Maximum Volume Clustering: A New Discriminative Clustering Approach
Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama; (45):2641−2687, 2013.
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Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
Aleks, r Y. Aravkin, James V. Burke, Gianluigi Pillonetto; (46):2689−2728, 2013.
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Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling
Shusen Wang, Zhihua Zhang; (47):2729−2769, 2013.
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Training Energy-Based Models for Time-Series Imputation
Phil\'{e}mon Brakel, Dirk Stroob, t, Benjamin Schrauwen; (48):2771−2797, 2013.
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Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees
Nima Noorshams, Martin J. Wainwright; (49):2799−2835, 2013.
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A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems
Daniil Ryabko, Jérémie Mary; (50):2837−2856, 2013.
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Manfred Opper, Ulrich Paquet, Ole Winther; (51):2857−2898, 2013.
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The CAM Software for Nonnegative Blind Source Separation in R-Java
Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; (52):2899−2903, 2013.
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A Near-Optimal Algorithm for Differentially-Private Principal Components
Kamalika Chaudhuri, An, D. Sarwate; (53):2905−2943, 2013.
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Parallel Vector Field Embedding
Binbin Lin, Xiaofei He, Chiyuan Zhang, Ming Ji; (54):2945−2977, 2013.
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Multi-Stage Multi-Task Feature Learning
Pinghua Gong, Jieping Ye, Changshui Zhang; (55):2979−3010, 2013.
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A Plug-in Approach to Neyman-Pearson Classification
Xin Tong; (56):3011−3040, 2013.
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Experiment Selection for Causal Discovery
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer; (57):3041−3071, 2013.
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Stationary-Sparse Causality Network Learning
Yuejia He, Yiyuan She, Dapeng Wu; (58):3073−3104, 2013.
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Algorithms and Hardness Results for Parallel Large Margin Learning
Philip M. Long, Rocco A. Servedio; (59):3105−3128, 2013.
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Large-scale SVD and Manifold Learning
Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry Rowley; (60):3129−3152, 2013.
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QuantMiner for Mining Quantitative Association Rules
Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; (61):3153−3157, 2013.
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Divvy: Fast and Intuitive Exploratory Data Analysis
Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; (62):3159−3163, 2013.
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Variational Algorithms for Marginal MAP
Qiang Liu, Alexander Ihler; (63):3165−3200, 2013.
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GURLS: A Least Squares Library for Supervised Learning
Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; (64):3201−3205, 2013.
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Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson; (65):3207−3260, 2013.
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Multivariate Convex Regression with Adaptive Partitioning
Lauren A. Hannah, David B. Dunson; (66):3261−3294, 2013.
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Fast MCMC Sampling for Markov Jump Processes and Extensions
Vinayak Rao, Yee Whye Teh; (67):3295−3320, 2013.
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Communication-Efficient Algorithms for Statistical Optimization
Yuchen Zhang, John C. Duchi, Martin J. Wainwright; (68):3321−3363, 2013.
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PC Algorithm for Nonparanormal Graphical Models
Naftali Harris, Mathias Drton; (69):3365−3383, 2013.
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Sparse Matrix Inversion with Scaled Lasso
Tingni Sun, Cun-Hui Zhang; (70):3385−3418, 2013.
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Consistent Selection of Tuning Parameters via Variable Selection Stability
Wei Sun, Junhui Wang, Yixin Fang; (71):3419−3440, 2013.
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Learning Theory Analysis for Association Rules and Sequential Event Prediction
Cynthia Rudin, Benjamin Letham, David Madigan; (72):3441−3492, 2013.
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Comment on "Robustness and Regularization of Support Vector Machines" by H. Xu et al. (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009)
Yahya Forghani, Hadi Sadoghi; (73):3493−3494, 2013.
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Lovasz theta function, SVMs and Finding Dense Subgraphs
Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi; (74):3495−3536, 2013.
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Learning Trees from Strings: A Strong Learning Algorithm for some Context-Free Grammars
Alexander Clark; (75):3537−3559, 2013.
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Classifying With Confidence From Incomplete Information
Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun Yu Hsiao; (76):3561−3589, 2013.
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Classifier Selection using the Predicate Depth
Ran Gilad-Bachrach, Christopher J.C. Burges; (77):3591−3618, 2013.
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A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
Tony Cai, Wen-Xin Zhou; (78):3619−3647, 2013.
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Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming
John Ahlgren, Shiu Yin Yuen; (79):3649−3681, 2013.
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How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
Alberto N. Escalante-B., Laurenz Wiskott; (80):3683−3719, 2013.
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Joint Harmonic Functions and Their Supervised Connections
Mark Vere Culp, Kenneth Joseph Ryan; (81):3721−3752, 2013.
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Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels
Kenji Fukumizu, Le Song, Arthur Gretton; (82):3753−3783, 2013.
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Optimally Fuzzy Temporal Memory
Karthik H. Shankar, Marc W. Howard; (83):3785−3812, 2013.
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BudgetedSVM: A Toolbox for Scalable SVM Approximations
Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; (84):3813−3817, 2013. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

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