JMLR Volume 10

• Exploring Strategies for Training Deep Neural Networks

  Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin (1):1−40, 2009 PDF BibTeX

• Markov Properties for Linear Causal Models with Correlated Errors

  Changsung Kang, Jin Tian (2):41−70, 2009 PDF BibTeX

• An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs

  M. Pawan Kumar, Vladimir Kolmogorov, Philip H.S. Torr (3):71−106, 2009 PDF BibTeX

• Refinement of Reproducing Kernels

  Yuesheng Xu, Haizhang Zhang (4):107−140, 2009 PDF BibTeX

• Subgroup Analysis via Recursive Partitioning

  Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li (5):141−158, 2009 PDF BibTeX

• Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

  Abhik Shah, Peter Woolf (6):159−162, 2009 PDF BibTeX

• On The Power of Membership Queries in Agnostic Learning

  Vitaly Feldman (7):163−182, 2009 PDF BibTeX

• Using Local Dependencies within Batches to Improve Large Margin Classifiers

  Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, Bharat Rao (8):183−206, 2009 PDF BibTeX

• Distance Metric Learning for Large Margin Nearest Neighbor Classification

  Kilian Q. Weinberger, Lawrence K. Saul (9):207−244, 2009 PDF BibTeX

• Data-driven Calibration of Penalties for Least-Squares Regression

  Sylvain Arlot, Pascal Massart (10):245−279, 2009 PDF BibTeX

• Analysis of Perceptron-Based Active Learning

  Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni (11):281−299, 2009 PDF BibTeX

• Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation

  Facundo Bromberg, Dimitris Margaritis (12):301−340, 2009 PDF BibTeX

• Low-Rank Kernel Learning with Bregman Matrix Divergences

  Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon (13):341−376, 2009 PDF BibTeX

• Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

  Petra Kralj Novak, Nada Lavrač, Geoffrey I. Webb (14):377−403, 2009 PDF BibTeX

• Particle Swarm Model Selection

  Hugo Jair Escalante, Manuel Montes, Luis Enrique Sucar (15):405−440, 2009 PDF BibTeX

• Generalization Bounds for Ranking Algorithms via Algorithmic Stability

  Shivani Agarwal, Partha Niyogi (16):441−474, 2009 PDF BibTeX

• Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm

  Junning Li, Z. Jane Wang (17):475−514, 2009 PDF BibTeX

• Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques

  Barnabás Póczos, András Loőrincz (18):515−554, 2009 PDF BibTeX

• On the Consistency of Feature Selection using Greedy Least Squares Regression

  Tong Zhang (19):555−568, 2009 PDF BibTeX

• Online Learning with Sample Path Constraints

  Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu (20):569−590, 2009 PDF BibTeX

• NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM

  Pradip Ghanty, Samrat Paul, Nikhil R. Pal (21):591−622, 2009 PDF BibTeX

• Scalable Collaborative Filtering Approaches for Large Recommender Systems

  Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk (22):623−656, 2009 PDF BibTeX

• Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions

  Sébastien Bubeck, Ulrike von Luxburg (23):657−698, 2009 PDF BibTeX

• Properties of Monotonic Effects on Directed Acyclic Graphs

  Tyler J. VanderWeele, James M. Robins (24):699−718, 2009 PDF BibTeX

• On Efficient Large Margin Semisupervised Learning: Method and Theory

  Junhui Wang, Xiaotong Shen, Wei Pan (25):719−742, 2009 PDF BibTeX

• Nieme: Large-Scale Energy-Based Models

  Francis Maes (26):743−746, 2009 PDF BibTeX

• Similarity-based Classification: Concepts and Algorithms

  Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti (27):747−776, 2009 PDF BibTeX

• Sparse Online Learning via Truncated Gradient

  John Langford, Lihong Li, Tong Zhang (28):777−801, 2009 PDF BibTeX

• A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

  Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-Philippe Vert (29):803−826, 2009 PDF BibTeX

• Consistency and Localizability

  Alon Zakai, Ya'acov Ritov (30):827−856, 2009 PDF BibTeX

• Stable and Efficient Gaussian Process Calculations

  Leslie Foster, Alex Waagen, Nabeela Aijaz, Michael Hurley, Apolonio Luis, Joel Rinsky, Chandrika Satyavolu, Michael J. Way, Paul Gazis, Ashok Srivastava (31):857−882, 2009 PDF BibTeX

• Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods

  Holger Höfling, Robert Tibshirani (32):883−906, 2009 PDF BibTeX

• Polynomial-Delay Enumeration of Monotonic Graph Classes

  Jan Ramon, Siegfried Nijssen (33):907−929, 2009 PDF BibTeX

• Java-ML: A Machine Learning Library

  Thomas Abeel, Yves Van de Peer, Yvan Saeys (34):931−934, 2009 PDF BibTeX

• Nonextensive Information Theoretic Kernels on Measures

  André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo (35):935−975, 2009 PDF BibTeX

• On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality

  Wenxin Jiang (36):977−996, 2009 PDF BibTeX

• Fourier Theoretic Probabilistic Inference over Permutations

  Jonathan Huang, Carlos Guestrin, Leonidas Guibas (37):997−1070, 2009 PDF BibTeX

• An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity

  Jose M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér (38):1071−1094, 2009 PDF BibTeX

• Universal Kernel-Based Learning with Applications to Regular Languages

  Leonid (Aryeh) Kontorovich, Boaz Nadler (39):1095−1129, 2009 PDF BibTeX

• Multi-task Reinforcement Learning in Partially Observable Stochastic Environments

  Hui Li, Xuejun Liao, Lawrence Carin (40):1131−1186, 2009 PDF BibTeX

• The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models

  Ricardo Silva, Zoubin Ghahramani (41):1187−1238, 2009 PDF BibTeX

• Incorporating Functional Knowledge in Neural Networks

  Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia (42):1239−1262, 2009 PDF BibTeX

• Perturbation Corrections in Approximate Inference: Mixture Modelling Applications

  Ulrich Paquet, Ole Winther, Manfred Opper (43):1263−1304, 2009 PDF BibTeX

• Robust Process Discovery with Artificial Negative Events

  Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens (44):1305−1340, 2009 PDF BibTeX

• Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination

  Eugene Tuv, Alexander Borisov, George Runger, Kari Torkkola (45):1341−1366, 2009 PDF BibTeX

• A Parameter-Free Classification Method for Large Scale Learning

  Marc Boullé (46):1367−1385, 2009 PDF BibTeX

• Model Monitor (M2): Evaluating, Comparing, and Monitoring Models

  Troy Raeder, Nitesh V. Chawla (47):1387−1390, 2009 PDF BibTeX

• A Least-squares Approach to Direct Importance Estimation

  Takafumi Kanamori, Shohei Hido, Masashi Sugiyama (48):1391−1445, 2009 PDF BibTeX

• Classification with Gaussians and Convex Loss

  Dao-Hong Xiang, Ding-Xuan Zhou (49):1447−1468, 2009 PDF BibTeX

• Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks

  Jean Hausser, Korbinian Strimmer (50):1469−1484, 2009 PDF BibTeX

• Robustness and Regularization of Support Vector Machines

  Huan Xu, Constantine Caramanis, Shie Mannor (51):1485−1510, 2009 PDF BibTeX

• Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks

  Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras (52):1511−1526, 2009 PDF BibTeX

• Bayesian Network Structure Learning by Recursive Autonomy Identification

  Raanan Yehezkel, Boaz Lerner (53):1527−1570, 2009 PDF BibTeX

• Learning Linear Ranking Functions for Beam Search with Application to Planning

  Yuehua Xu, Alan Fern, Sungwook Yoon (54):1571−1610, 2009 PDF BibTeX

• Marginal Likelihood Integrals for Mixtures of Independence Models

  Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu (55):1611−1631, 2009 PDF BibTeX

• Transfer Learning for Reinforcement Learning Domains: A Survey

  Matthew E. Taylor, Peter Stone (56):1633−1685, 2009 PDF BibTeX

• Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification

  Eitan Greenshtein, Junyong Park (57):1687−1704, 2009 PDF BibTeX

• Learning Permutations with Exponential Weights

  David P. Helmbold, Manfred K. Warmuth (58):1705−1736, 2009 PDF BibTeX

• SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

  Antoine Bordes, Léon Bottou, Patrick Gallinari (59):1737−1754, 2009 PDF BibTeX

• Dlib-ml: A Machine Learning Toolkit

  Davis E. King (60):1755−1758, 2009 PDF BibTeX

• Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning

  Halbert White, Karim Chalak (61):1759−1799, 2009 PDF BibTeX

• Distributed Algorithms for Topic Models

  David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling (62):1801−1828, 2009 PDF BibTeX

• Nonlinear Models Using Dirichlet Process Mixtures

  Babak Shahbaba, Radford Neal (63):1829−1850, 2009 PDF BibTeX

• CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning

  Roberto Esposito, Daniele P. Radicioni (64):1851−1880, 2009 PDF BibTeX

• Learning Acyclic Probabilistic Circuits Using Test Paths

  Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin (65):1881−1911, 2009 PDF BibTeX

• Learning Approximate Sequential Patterns for Classification

  Zeeshan Syed, Piotr Indyk, John Guttag (66):1913−1936, 2009 PDF BibTeX

• Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training

  Kristian Woodsend, Jacek Gondzio (67):1937−1953, 2009 PDF BibTeX

• Provably Efficient Learning with Typed Parametric Models

  Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy (68):1955−1988, 2009 PDF BibTeX

• Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection

  Jie Chen, Haw-ren Fang, Yousef Saad (69):1989−2012, 2009 PDF BibTeX

• Ultrahigh Dimensional Feature Selection: Beyond The Linear Model

  Jianqing Fan, Richard Samworth, Yichao Wu (70):2013−2038, 2009 PDF BibTeX

• Evolutionary Model Type Selection for Global Surrogate Modeling

  Dirk Gorissen, Tom Dhaene, Filip De Turck (71):2039−2078, 2009 PDF BibTeX

• An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems

  Luciana Ferrer, Kemal Sönmez, Elizabeth Shriberg (72):2079−2114, 2009 PDF BibTeX

• Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods

  Christian Rieger, Barbara Zwicknagl (73):2115−2132, 2009 PDF BibTeX

• RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments

  Brian Tanner, Adam White (74):2133−2136, 2009 PDF BibTeX

• Discriminative Learning Under Covariate Shift

  Steffen Bickel, Michael Brückner, Tobias Scheffer (75):2137−2155, 2009 PDF BibTeX

• Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

  Vojtěch Franc, Sören Sonnenburg (76):2157−2192, 2009 PDF BibTeX

• Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

  Cynthia Rudin, Robert E. Schapire (77):2193−2232, 2009 PDF BibTeX

• The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

  Cynthia Rudin (78):2233−2271, 2009 PDF BibTeX

• Learning Nondeterministic Classifiers

  Juan José del Coz, Jorge Díez, Antonio Bahamonde (79):2273−2293, 2009 PDF BibTeX

• The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

  Han Liu, John Lafferty, Larry Wasserman (80):2295−2328, 2009 PDF BibTeX

• Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors

  Mathias Drton, Michael Eichler, Thomas S. Richardson (81):2329−2348, 2009 PDF BibTeX

• Estimating Labels from Label Proportions

  Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le (82):2349−2374, 2009 PDF BibTeX

• Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions

  Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page (83):2375−2411, 2009 PDF BibTeX

• Reinforcement Learning in Finite MDPs: PAC Analysis

  Alexander L. Strehl, Lihong Li, Michael L. Littman (84):2413−2444, 2009 PDF BibTeX

• Prediction With Expert Advice For The Brier Game

  Vladimir Vovk, Fedor Zhdanov (85):2445−2471, 2009 PDF BibTeX

• Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression

  Saharon Rosset (86):2473−2505, 2009 PDF BibTeX

• When Is There a Representer Theorem? Vector Versus Matrix Regularizers

  Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil (87):2507−2529, 2009 PDF BibTeX

• Maximum Entropy Discrimination Markov Networks

  Jun Zhu, Eric P. Xing (88):2531−2569, 2009 PDF BibTeX

• Learning When Concepts Abound

  Omid Madani, Michael Connor, Wiley Greiner (89):2571−2613, 2009 PDF BibTeX

• Hash Kernels for Structured Data

  Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan (90):2615−2637, 2009 PDF BibTeX

• DL-Learner: Learning Concepts in Description Logics

  Jens Lehmann (91):2639−2642, 2009 PDF BibTeX

• Bounded Kernel-Based Online Learning

  Francesco Orabona, Joseph Keshet, Barbara Caputo (92):2643−2666, 2009 PDF BibTeX

• Structure Spaces

  Brijnesh J. Jain, Klaus Obermayer (93):2667−2714, 2009 PDF BibTeX

• Learning Halfspaces with Malicious Noise

  Adam R. Klivans, Philip M. Long, Rocco A. Servedio (94):2715−2740, 2009 PDF BibTeX

• Reproducing Kernel Banach Spaces for Machine Learning

  Haizhang Zhang, Yuesheng Xu, Jun Zhang (95):2741−2775, 2009 PDF BibTeX

• Cautious Collective Classification

  Luke K. McDowell, Kalyan Moy Gupta, David W. Aha (96):2777−2836, 2009 PDF BibTeX

• Adaptive False Discovery Rate Control under Independence and Dependence

  Gilles Blanchard, Étienne Roquain (97):2837−2871, 2009 PDF BibTeX

• Online Learning with Samples Drawn from Non-identical Distributions

  Ting Hu, Ding-Xuan Zhou (98):2873−2898, 2009 PDF BibTeX

• Efficient Online and Batch Learning Using Forward Backward Splitting

  John Duchi, Yoram Singer (99):2899−2934, 2009 PDF BibTeX

• A Survey of Accuracy Evaluation Metrics of Recommendation Tasks

  Asela Gunawardana, Guy Shani (100):2935−2962, 2009 PDF BibTeX