JMLR Volume 14
- Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization
- Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka; (1):1−37, 2013.
[abs][pdf][bib]
- Ranking Forests
- Stéphan Clémençon, Marine Depecker, Nicolas Vayatis; (2):39−73, 2013.
[abs][pdf][bib]
- Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
- Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari; (3):75−109, 2013.
[abs][pdf][bib]
- Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models
- Aapo Hyvärinen, Stephen M. Smith; (4):111−152, 2013.
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- Universal Consistency of Localized Versions of Regularized Kernel Methods
- Robert Hable; (5):153−186, 2013.
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- Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem
- Antoine Salomon, Jean-Yves Audibert, Issam El Alaoui; (6):187−207, 2013.
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- MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures With Low Probability of False Positives During Use
- Daniel Kyu Hwa Kohlsdorf, Thad E. Starner; (7):209−242, 2013.
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- Sparse Single-Index Model
- Pierre Alquier, Gérard Biau; (8):243−280, 2013.
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- Using Symmetry and Evolutionary Search to Minimize Sorting Networks
- Vinod K. Valsalam, Risto Miikkulainen; (9):303−331, 2013.
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- A Framework for Evaluating Approximation Methods for Gaussian Process Regression
- Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray; (10):303−331, 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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- Ranked Bandits in Metric Spaces: Learning Diverse Rankings over Large Document Collections
- Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi; (13):399−436, 2013.
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- A Theory of Multiclass Boosting
- Indraneel Mukherjee, Robert E. Schapire; (14):437−497, 2013.
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- Algorithms for Discovery of Multiple Markov Boundaries
- Alexander Statnikov, Nikita I. Lytkin, Jan Lemeire, Constantin F. Aliferis; (15):499−566, 2013.
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- Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
- Shai Shalev-Shwartz, Tong Zhang; (16):567−599, 2013.
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- Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality
- Sébastien Bubeck, Damien Ernst, Aurélien Garivier; (17):601−623, 2013.
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- A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics
- Hervé Frezza-Buet, Matthieu Geist; (18):625−628, 2013. (Machine Learning Open Source Software Paper)
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[code]
- CODA: High Dimensional Copula Discriminant Analysis
- Fang Han, Tuo Zhao, Han Liu; (19):629−671, 2013.
[abs][pdf][bib]
- Bayesian Nonparametric Hidden Semi-Markov Models
- Matthew J. Johnson, Alan S. Willsky; (20):673−701, 2013.
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- Differential Privacy for Functions and Functional Data
- Rob Hall, Alessandro Rinaldo, Larry Wasserman; (21):703−727, 2013.
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- Sparsity Regret Bounds for Individual Sequences in Online Linear Regression
- Sébastien Gerchinovitz; (22):729−769, 2013.
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- Semi-Supervised Learning Using Greedy Max-Cut
- Jun Wang, Tony Jebara, Shih-Fu Chang; (23):771−800, 2013.
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- MLPACK: A Scalable C++ Machine Learning Library
- Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; (24):801−805, 2013. (Machine Learning Open Source Software Paper)
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[code]
- Greedy Sparsity-Constrained Optimization
- Sohail Bahmani, Bhiksha Raj, Petros T. Boufounos; (25):807−841, 2013.
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- Quasi-Newton Method: A New Direction
- Philipp Hennig, Martin Kiefel; (26):843−865, 2013.
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- A Widely Applicable Bayesian Information Criterion
- Sumio Watanabe; (27):867−897, 2013.
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- Truncated Power Method for Sparse Eigenvalue Problems
- Xiao-Tong Yuan, Tong Zhang; (28):899−925, 2013.
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- Query Induction with Schema-Guided Pruning Strategies
- Joachim Niehren, Jérôme Champavère, Aurélien Lemay, Rémi Gilleron; (29):927−964, 2013.
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- Bayesian Canonical Correlation Analysis
- Arto Klami, Seppo Virtanen, Samuel Kaski; (30):965−1003, 2013.
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- Bayesian Canonical Correlation Analysis
- Chong Wang, David M. Blei; (31):965−1003, 2013.
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- Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications
- Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock, Gavin Brown; (32):1033−1090, 2013.
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- Sparse Activity and Sparse Connectivity in Supervised Learning
- Markus Thom, Günther Palm; (33):1091−1143, 2013.
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- Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing
- Lisha Chen, Andreas Buja; (34):1145−1173, 2013.
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- GPstuff: Bayesian Modeling with Gaussian Processes
- Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari; (35):1175−1179, 2013. (Machine Learning Open Source Software Paper)
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[code]
- Performance Bounds for λ Policy Iteration and Application to the Game of Tetris
- Bruno Scherrer; (36):1181−1227, 2013.
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- Manifold Regularization and Semi-supervised Learning: Some Theoretical Analyses
- Partha Niyogi; (37):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; (38):1251−1284, 2013.
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- Regularization-Free Principal Curve Estimation
- Samuel Gerber, Ross Whitaker; (39):1285−1302, 2013.
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- Stochastic Variational Inference
- Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley; (40):1303−1347, 2013.
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- Multicategory Large-Margin Unified Machines
- Chong Zhang, Yufeng Liu; (41):1349−1386, 2013.
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- Finding Optimal Bayesian Networks Using Precedence Constraints
- Pekka Parviainen, Mikko Koivisto; (42):1387−1415, 2013.
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- JKernelMachines: A Simple Framework for Kernel Machines
- David Picard, Nicolas Thome, Matthieu Cord; (43):1417−1421, 2013. (Machine Learning Open Source Software Paper)
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[code]
- Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators
- Pierre Neuvial; (44):1423−1459, 2013.
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- Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems
- Takafumi Kanamori, Akiko Takeda, Taiji Suzuki; (45):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; (46):1505−1511, 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
- Philémon Brakel, Dirk Stroobandt, Benjamin Schrauwen; (48):2771−2797, 2013.
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- Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion
- Rami Mahdi, Jason Mezey; (49):1563−1603, 2013.
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- Dimension Independent Similarity Computation
- Reza Bosagh Zadeh, Ashish Goel; (50):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; (51):1627−1663, 2013.
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- Nonparametric Sparsity and Regularization
- Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Aless, ro Verri; (52):1665−1714, 2013.
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- Similarity-based Clustering by Left-Stochastic Matrix Factorization
- Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel; (53):1715−1746, 2013.
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- On the Convergence of Maximum Variance Unfolding
- Ery Arias-Castro, Bruno Pelletier; (54):1747−1770, 2013.
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- Variable Selection in High-Dimension with Random Designs and Orthogonal Matching Pursuit
- Antony Joseph; (55):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; (56):1801−1835, 2013.
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- Distributions of Angles in Random Packing on Spheres
- Tony Cai, Jianqing Fan, Tiefeng Jiang; (57):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; (58):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; (59):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; (60):1947−1988, 2013.
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- Machine Learning with Operational Costs
- Theja Tulab, hula, Cynthia Rudin; (61):1989−2028, 2013.
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- Approximating the Permanent with Fractional Belief Propagation
- Michael Chertkov, Adam B. Yedidia; (62):2029−2066, 2013.
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- Construction of Approximation Spaces for Reinforcement Learning
- Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer; (63):2067−2118, 2013.
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- Distribution-Dependent Sample Complexity of Large Margin Learning
- Sivan Sabato, Nathan Srebro, Naftali Tishby; (64):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; (65):2151−2188, 2013.
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- Language-Motivated Approaches to Action Recognition
- Manavender R. Malgireddy, Ifeoma Nwogu, Venu Govindaraju; (66):2189−2212, 2013.
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- Segregating Event Streams and Noise with a Markov Renewal Process Model
- Dan Stowell, Mark D. Plumbley; (67):2213−2238, 2013.
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- Gaussian Kullback-Leibler Approximate Inference
- Edward Challis, David Barber; (68):2239−2286, 2013.
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- Message-Passing Algorithms for Quadratic Minimization
- Nicholas Ruozzi, Sekhar Tatikonda; (69):2287−2314, 2013.
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- The Rate of Convergence of AdaBoost
- Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire; (70):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; (71):2349−2353, 2013. (Machine Learning Open Source Software Paper)
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[code]
- Tapkee: An Efficient Dimension Reduction Library
- Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; (72):2355−2359, 2013. (Machine Learning Open Source Software Paper)
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[code]
- On the Mutual Nearest Neighbors Estimate in Regression
- Arnaud Guyader, Nick Hengartner; (73):2361−2376, 2013.
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- Distance Preserving Embeddings for General n-Dimensional Manifolds
- Nakul Verma; (74):2415−2448, 2013.
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- Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
- Julien Mairal, Bin Yu; (75):2449−2485, 2013.
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- Greedy Feature Selection for Subspace Clustering
- Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk; (76):2487−2517, 2013.
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- Learning Bilinear Model for Matching Queries and Documents
- Wei Wu, Zhengdong Lu, Hang Li; (77):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; (78):2549−2582, 2013.
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- Efficient Active Learning of Halfspaces: An Aggressive Approach
- Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz; (79):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; (80):2617−2640, 2013.
[abs][pdf][bib]
- Maximum Volume Clustering: A New Discriminative Clustering Approach
- Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama; (81):2641−2687, 2013.
[abs][pdf][bib]
- Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
- Aleks, r Y. Aravkin, James V. Burke, Gianluigi Pillonetto; (82):2689−2728, 2013.
[abs][pdf][bib]
- 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]
- Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees
- Nima Noorshams, Martin J. Wainwright; (85):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; (86):2837−2856, 2013.
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- Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
- Manfred Opper, Ulrich Paquet, Ole Winther; (87):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; (88):2899−2903, 2013. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- A Near-Optimal Algorithm for Differentially-Private Principal Components
- Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha; (89):2905−2943, 2013.
[abs][pdf][bib]
- Parallel Vector Field Embedding
- Binbin Lin, Xiaofei He, Chiyuan Zhang, Ming Ji; (90):2945−2977, 2013.
[abs][pdf][bib]
- Multi-Stage Multi-Task Feature Learning
- Pinghua Gong, Jieping Ye, Changshui Zhang; (91):2979−3010, 2013.
[abs][pdf][bib]
- A Plug-in Approach to Neyman-Pearson Classification
- Xin Tong; (92):3011−3040, 2013.
[abs][pdf][bib]
- Experiment Selection for Causal Discovery
- Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer; (93):3041−3071, 2013.
[abs][pdf][bib]
- Stationary-Sparse Causality Network Learning
- Yuejia He, Yiyuan She, Dapeng Wu; (94):3073−3104, 2013.
[abs][pdf][bib]
- Algorithms and Hardness Results for Parallel Large Margin Learning
- Philip M. Long, Rocco A. Servedio; (95):3105−3128, 2013.
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- Large-scale SVD and Manifold Learning
- Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry Rowley; (96):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; (97):3153−3157, 2013. (Machine Learning Open Source Software Paper)
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[code]
- Divvy: Fast and Intuitive Exploratory Data Analysis
- Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; (98):3159−3163, 2013. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
[webpage]
- Using Symmetry and Evolutionary Search to Minimize Sorting Networks
- Qiang Liu, Alexander Ihler; (99):303−331, 2013.
[abs][pdf][bib]
- GURLS: A Least Squares Library for Supervised Learning
- Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; (100):3201−3205, 2013. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- 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; (101):3207−3260, 2013.
[abs][pdf][bib]
- Multivariate Convex Regression with Adaptive Partitioning
- Lauren A. Hannah, David B. Dunson; (102):3261−3294, 2013.
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- Fast MCMC Sampling for Markov Jump Processes and Extensions
- Vinayak Rao, Yee Whye Teh; (103):3295−3320, 2013.
[abs][pdf][bib]
- Communication-Efficient Algorithms for Statistical Optimization
- Yuchen Zhang, John C. Duchi, Martin J. Wainwright; (104):3321−3363, 2013.
[abs][pdf][bib]
- PC Algorithm for Nonparanormal Graphical Models
- Naftali Harris, Mathias Drton; (105):3365−3383, 2013.
[abs][pdf][bib]
- Sparse Matrix Inversion with Scaled Lasso
- Tingni Sun, Cun-Hui Zhang; (106):3385−3418, 2013.
[abs][pdf][bib]
- Consistent Selection of Tuning Parameters via Variable Selection Stability
- Wei Sun, Junhui Wang, Yixin Fang; (107):3419−3440, 2013.
[abs][pdf][bib]
- Learning Theory Analysis for Association Rules and Sequential Event Prediction
- Cynthia Rudin, Benjamin Letham, David Madigan; (108):3441−3492, 2013.
[abs][pdf][bib]
- 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; (109):3493−3494, 2013.
[abs][pdf][bib]
- Lovasz theta function, SVMs and Finding Dense Subgraphs
- Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi; (110):3495−3536, 2013.
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- Learning Trees from Strings: A Strong Learning Algorithm for some Context-Free Grammars
- Alexander Clark; (111):3537−3559, 2013.
[abs][pdf][bib]
- Classifying With Confidence From Incomplete Information
- Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun Yu Hsiao; (112):3561−3589, 2013.
[abs][pdf][bib]
- Classifier Selection using the Predicate Depth
- Ran Gilad-Bachrach, Christopher J.C. Burges; (113):3591−3618, 2013.
[abs][pdf][bib]
- A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
- Tony Cai, Wen-Xin Zhou; (114):3619−3647, 2013.
[abs][pdf][bib]
- Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming
- John Ahlgren, Shiu Yin Yuen; (115):3649−3681, 2013.
[abs][pdf][bib]
- 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; (116):3683−3719, 2013.
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- Joint Harmonic Functions and Their Supervised Connections
- Mark Vere Culp, Kenneth Joseph Ryan; (117):3721−3752, 2013.
[abs][pdf][bib]
- Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels
- Kenji Fukumizu, Le Song, Arthur Gretton; (118):3753−3783, 2013.
[abs][pdf][bib]
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