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JMLR Volume 11

An Efficient Explanation of Individual Classifications using Game Theory: Erik Štrumbelj, Igor Kononenko; (1):1−18, 2010.
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Online Learning for Matrix Factorization and Sparse Coding: Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro; (2):19−60, 2010.
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Model Selection: Beyond the Bayesian/Frequentist Divide: Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley; (3):61−87, 2010.
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On-Line Sequential Bin Packing: András György, Gábor Lugosi, György Ottucsàk; (4):89−109, 2010.
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Classification Methods with Reject Option Based on Convex Risk Minimization: Ming Yuan, Marten Wegkamp; (5):111−130, 2010.
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An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data: Yufeng Ding, Jeffrey S. Simonoff; (6):131−170, 2010.
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation: Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos; (7):171−234, 2010.
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions: Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos; (8):235−284, 2010.
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Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure: Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano; (9):285−310, 2010.
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Bundle Methods for Regularized Risk Minimization: Choon Hui Teo, S.V.N. Vishwanthan, Alex J. Smola, Quoc V. Le; (10):311−365, 2010.
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A Convergent Online Single Time Scale Actor Critic Algorithm: Dotan Di Castro, Ron Meir; (11):367−410, 2010.
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Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting: Philippos Mordohai, Gérard Medioni; (12):411−450, 2010.
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Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization: Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski; (13):451−490, 2010.
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Classification Using Geometric Level Sets: Kush R. Varshney, Alan S. Willsky; (14):491−516, 2010.
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Generalized Power Method for Sparse Principal Component Analysis: Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre; (15):517−553, 2010.
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Approximate Tree Kernels: Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller; (16):555−580, 2010.
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On Finding Predictors for Arbitrary Families of Processes: Daniil Ryabko; (17):581−602, 2010.
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A Rotation Test to Verify Latent Structure: Patrick O. Perry, Art B. Owen; (18):603−624, 2010.
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Why Does Unsupervised Pre-training Help Deep Learning?: Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio; (19):625−660, 2010.
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Error-Correcting Output Codes Library: Sergio Escalera, Oriol Pujol, Petia Radeva; (20):661−664, 2010. (Machine Learning Open Source Software Paper)
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Second-Order Bilinear Discriminant Analysis: Christoforos Christoforou, Robert Haralick, Paul Sajda, Lucas C. Parra; (21):665−685, 2010.
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On the Rate of Convergence of the Bagged Nearest Neighbor Estimate: Gérard Biau, Frédéric Cérou, Arnaud Guyader; (22):687−712, 2010.
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A Fast Hybrid Algorithm for Large-Scale l₁-Regularized Logistic Regression: Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda; (23):713−741, 2010.
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PyBrain: Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber; (24):743−746, 2010.
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Maximum Relative Margin and Data-Dependent Regularization: Pannagadatta K. Shivaswamy, Tony Jebara; (25):747−788, 2010.
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Stability Bounds for Stationary φ-mixing and β-mixing Processes: Mehryar Mohri, Afshin Rostamizadeh; (26):789−814, 2010.
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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models: Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin; (27):815−848, 2010.
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A Streaming Parallel Decision Tree Algorithm: Yael Ben-Haim, Elad Tom-Tov; (28):849−872, 2010.
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Image Denoising with Kernels Based on Natural Image Relations: Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo; (29):873−903, 2010.
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On Learning with Integral Operators: Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito; (30):905−934, 2010.
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On Spectral Learning: Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil; (31):935−953, 2010.
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Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data: Gideon S. Mann, Andrew McCallum; (32):955−984, 2010.
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Kronecker Graphs: An Approach to Modeling Networks: Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani; (33):985−1042, 2010.
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Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes: Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright; (34):1043−1080, 2010.
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Analysis of Multi-stage Convex Relaxation for Sparse Regularization: Tong Zhang; (35):1081−1107, 2010.
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Large Scale Online Learning of Image Similarity Through Ranking: Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio; (36):1109−1135, 2010.
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Continuous Time Bayesian Network Reasoning and Learning Engine: Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; (37):1137−1140, 2010. (Machine Learning Open Source Software Paper)
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SFO: A Toolbox for Submodular Function Optimization: Andreas Krause; (38):1141−1144, 2010. (Machine Learning Open Source Software Paper)
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A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning: Jin Yu, S.V.N. Vishwanathan, Simon Günter, Nicol N. Schraudolph; (39):1145−1200, 2010.
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Graph Kernels: S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt; (40):1201−1242, 2010.
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Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation: Miki Aoyagi; (41):1243−1272, 2010.
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Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation: Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov; (42):1273−1296, 2010.
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Learning From Crowds: Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy; (43):1297−1322, 2010.
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Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels: Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian; (44):1323−1351, 2010.
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Learning Translation Invariant Kernels for Classification: Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi; (45):1353−1390, 2010.
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Consistent Nonparametric Tests of Independence: Arthur Gretton, László Györfi; (46):1391−1423, 2010.
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Characterization, Stability and Convergence of Hierarchical Clustering Methods: Gunnar Carlsson, Facundo Mémoli; (47):1425−1470, 2010.
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Training and Testing Low-degree Polynomial Data Mappings via Linear SVM: Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin; (48):1471−1490, 2010.
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Quadratic Programming Feature Selection: Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz; (49):1491−1516, 2010.
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Hilbert Space Embeddings and Metrics on Probability Measures: Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet; (50):1517−1561, 2010.
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Near-optimal Regret Bounds for Reinforcement Learning: Thomas Jaksch, Ronald Ortner, Peter Auer; (51):1563−1600, 2010.
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MOA: Massive Online Analysis: Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; (52):1601−1604, 2010. (Machine Learning Open Source Software Paper)
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On the Foundations of Noise-free Selective Classification: Ran El-Yaniv, Yair Wiener; (53):1605−1641, 2010.
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Introduction to Causal Inference: Peter Spirtes; (54):1643−1662, 2010.
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Consensus-Based Distributed Support Vector Machines: Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis; (55):1663−1707, 2010.
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Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity: Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer; (56):1709−1731, 2010.
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FastInf: An Efficient Approximate Inference Library: Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; (57):1733−1736, 2010. (Machine Learning Open Source Software Paper)
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Evolving Static Representations for Task Transfer: Phillip Verbancsics, Kenneth O. Stanley; (58):1737−1769, 2010.
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Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing: Ryo Yoshida, Mike West; (59):1771−1798, 2010.
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The SHOGUN Machine Learning Toolbox: Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojt{{\ve}}ch Franc; (60):1799−1802, 2010. (Machine Learning Open Source Software Paper)
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How to Explain Individual Classification Decisions: David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller; (61):1803−1831, 2010.
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Permutation Tests for Studying Classifier Performance: Markus Ojala, Gemma C. Garriga; (62):1833−1863, 2010.
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Sparse Spectrum Gaussian Process Regression: Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal; (63):1865−1881, 2010.
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Fast and Scalable Local Kernel Machines: Nicola Segata, Enrico Blanzieri; (64):1883−1926, 2010.
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Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes: Liva Ralaivola, Marie Szafranski, Guillaume Stempfel; (65):1927−1956, 2010.
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Practical Approaches to Principal Component Analysis in the Presence of Missing Values: Alexander Ilin, Tapani Raiko; (66):1957−2000, 2010.
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Posterior Regularization for Structured Latent Variable Models: Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar; (67):2001−2049, 2010.
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A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design: Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; (68):2051−2055, 2010. (Machine Learning Open Source Software Paper)
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Matrix Completion from Noisy Entries: Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh; (69):2057−2078, 2010.
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On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation: Gavin C. Cawley, Nicola L. C. Talbot; (70):2079−2107, 2010.
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Model-based Boosting 2.0: Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; (71):2109−2113, 2010. (Machine Learning Open Source Software Paper)
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Importance Sampling for Continuous Time Bayesian Networks: Yu Fan, Jing Xu, Christian R. Shelton; (72):2115−2140, 2010.
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Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases: Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Wang; (73):2141−2167, 2010.
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libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models: Joris M. Mooij; (74):2169−2173, 2010. (Machine Learning Open Source Software Paper)
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Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence: Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee; (75):2175−2198, 2010.
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Regularized Discriminant Analysis, Ridge Regression and Beyond: Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan; (76):2199−2228, 2010.
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Erratum: SGDQN is Less Careful than Expected: Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith; (77):2229−2240, 2010.
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Restricted Eigenvalue Properties for Correlated Gaussian Designs: Garvesh Raskutti, Martin J. Wainwright, Bin Yu; (78):2241−2259, 2010.
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High Dimensional Inverse Covariance Matrix Estimation via Linear Programming: Ming Yuan; (79):2261−2286, 2010.
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Spectral Regularization Algorithms for Learning Large Incomplete Matrices: Rahul Mazumder, Trevor Hastie, Robert Tibshirani; (80):2287−2322, 2010.
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Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers: Franz Pernkopf, Jeff A. Bilmes; (81):2323−2360, 2010.
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High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency: Dapo Omidiran, Martin J. Wainwright; (82):2361−2386, 2010.
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Composite Binary Losses: Mark D. Reid, Robert C. Williamson; (83):2387−2422, 2010.
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Sparse Semi-supervised Learning Using Conjugate Functions: Shiliang Sun, John Shawe-Taylor; (84):2423−2455, 2010.
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Rademacher Complexities and Bounding the Excess Risk in Active Learning: Vladimir Koltchinskii; (85):2457−2485, 2010.
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Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data: Miloš Radovanović, Alexandros Nanopoulos, Mirjana Ivanović; (86):2487−2531, 2010.
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WEKA−Experiences with a Java Open-Source Project: Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten; (87):2533−2541, 2010.
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Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization: Lin Xiao; (88):2543−2596, 2010.
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Stochastic Composite Likelihood: Joshua V. Dillon, Guy Lebanon; (89):2597−2633, 2010.
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Learnability, Stability and Uniform Convergence: Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan; (90):2635−2670, 2010.
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Topology Selection in Graphical Models of Autoregressive Processes: Jitkomut Songsiri, Lieven Vandenberghe; (91):2671−2705, 2010.
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Using Contextual Representations to Efficiently Learn Context-Free Languages: Alexander Clark, Rémi Eyraud, Amaury Habrard; (92):2707−2744, 2010.
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Mean Field Variational Approximation for Continuous-Time Bayesian Networks: Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman; (93):2745−2783, 2010.
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Regret Bounds and Minimax Policies under Partial Monitoring: Jean-Yves Audibert, Sébastien Bubeck; (94):2785−2836, 2010.
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Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance: Nguyen Xuan Vinh, Julien Epps, James Bailey; (95):2837−2854, 2010.
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Expectation Truncation and the Benefits of Preselection In Training Generative Models: Jörg Lücke, Julian Eggert; (96):2855−2900, 2010.
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Linear Algorithms for Online Multitask Classification: Giovanni Cavallanti, Nicoló Cesa-Bianchi, Claudio Gentile; (97):2901−2934, 2010.
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Tree Decomposition for Large-Scale SVM Problems: Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu; (98):2935−2972, 2010.
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Semi-Supervised Novelty Detection: Gilles Blanchard, Gyemin Lee, Clayton Scott; (99):2973−3009, 2010.
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Gaussian Processes for Machine Learning (GPML) Toolbox: Carl Edward Rasmussen, Hannes Nickisch; (100):3011−3015, 2010. (Machine Learning Open Source Software Paper)
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Covariance in Unsupervised Learning of Probabilistic Grammars: Shay B. Cohen, Noah A. Smith; (101):3017−3051, 2010.
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Inducing Tree-Substitution Grammars: Trevor Cohn, Phil Blunsom, Sharon Goldwater; (102):3053−3096, 2010.
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Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials: Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan; (103):3097−3135, 2010.
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A Generalized Path Integral Control Approach to Reinforcement Learning: Evangelos Theodorou, Jonas Buchli, Stefan Schaal; (104):3137−3181, 2010.
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A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification: Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin; (105):3183−3234, 2010.
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Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes: Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen; (106):3235−3268, 2010.
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Classification with Incomplete Data Using Dirichlet Process Priors: Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson; (107):3269−3311, 2010.
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Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds: Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra; (108):3313−3332, 2010.
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Learning Instance-Specific Predictive Models: Shyam Visweswaran, Gregory F. Cooper; (109):3333−3369, 2010.
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Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion: Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol; (110):3371−3408, 2010.
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L_p-Nested Symmetric Distributions: Fabian Sinz, Matthias Bethge; (111):3409−3451, 2010.
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Efficient Algorithms for Conditional Independence Inference: Remco Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený; (112):3453−3479, 2010.
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An Exponential Model for Infinite Rankings: Marina Meilă, Le Bao; (113):3481−3518, 2010.
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Rate Minimaxity of the Lasso and Dantzig Selector for the l_q Loss in l_r Balls: Fei Ye, Cun-Hui Zhang; (114):3519−3540, 2010.
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Incremental Sigmoid Belief Networks for Grammar Learning: James Henderson, Ivan Titov; (115):3541−3570, 2010.
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Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory: Sumio Watanabe; (116):3571−3594, 2010.
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PAC-Bayesian Analysis of Co-clustering and Beyond: Yevgeny Seldin, Naftali Tishby; (117):3595−3646, 2010.
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Learning Non-Stationary Dynamic Bayesian Networks: Joshua W. Robinson, Alexander J. Hartemink; (118):3647−3680, 2010.
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