JMLR Volume 10

Exploring Strategies for Training Deep Neural Networks
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin; 10(Jan):1--40, 2009.
[abs][pdf]

Markov Properties for Linear Causal Models with Correlated Errors    (Special Topic on Causality)
Changsung Kang, Jin Tian; 10(Jan):41--70, 2009.
[abs][pdf]

An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs
M. Pawan Kumar, Vladimir Kolmogorov, Philip H.S. Torr; 10(Jan):71--106, 2009.
[abs][pdf]

Refinement of Reproducing Kernels
Yuesheng Xu, Haizhang Zhang; 10(Jan):107--140, 2009.
[abs][pdf]

Subgroup Analysis via Recursive Partitioning
Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li; 10(Feb):141--158, 2009.
[abs][pdf]

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data    (Machine Learning Open Source Software Paper)
Abhik Shah, Peter Woolf; 10(Feb):159--162, 2009.
[abs][pdf]    [code][mloss.org]

On The Power of Membership Queries in Agnostic Learning
Vitaly Feldman; 10(Feb):163--182, 2009.
[abs][pdf]

Using Local Dependencies within Batches to Improve Large Margin Classifiers
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, Bharat Rao; 10(Feb):183--206, 2009.
[abs][pdf]

Distance Metric Learning for Large Margin Nearest Neighbor Classification
Kilian Q. Weinberger, Lawrence K. Saul; 10(Feb):207--244, 2009.
[abs][pdf]

Data-driven Calibration of Penalties for Least-Squares Regression
Sylvain Arlot, Pascal Massart; 10(Feb):245--279, 2009.
[abs][pdf]

Analysis of Perceptron-Based Active Learning
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni; 10(Feb):281--299, 2009.
[abs][pdf]

Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation    (Special Topic on Causality)
Facundo Bromberg, Dimitris Margaritis; 10(Feb):301--340, 2009.
[abs][pdf]

Low-Rank Kernel Learning with Bregman Matrix Divergences
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon; 10(Feb):341--376, 2009.
[abs][pdf]

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
Petra Kralj Novak, Nada Lavrač, Geoffrey I. Webb; 10(Feb):377--403, 2009.
[abs][pdf]

Particle Swarm Model Selection    (Special Topic on Model Selection)
Hugo Jair Escalante, Manuel Montes, Luis Enrique Sucar; 10(Feb):405--440, 2009.
[abs][pdf]

Generalization Bounds for Ranking Algorithms via Algorithmic Stability
Shivani Agarwal, Partha Niyogi; 10(Feb):441--474, 2009.
[abs][pdf]

Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm    (Special Topic on Mining and Learning with Graphs and Relations)
Junning Li, Z. Jane Wang; 10(Feb):475--514, 2009.
[abs][pdf]

Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques
Barnabás Póczos, András Lőrincz; 10(Feb):515--554, 2009.
[abs][pdf]

On the Consistency of Feature Selection using Greedy Least Squares Regression
Tong Zhang; 10(Mar):555--568, 2009.
[abs][pdf]

Online Learning with Sample Path Constraints
Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu; 10(Mar):569--590, 2009.
[abs][pdf]

NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Pradip Ghanty, Samrat Paul, Nikhil R. Pal; 10(Mar):591--622, 2009.
[abs][pdf]

Scalable Collaborative Filtering Approaches for Large Recommender Systems    (Special Topic on Mining and Learning with Graphs and Relations)
Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk; 10(Mar):623--656, 2009.
[abs][pdf]

Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions
Sébastien Bubeck, Ulrike von Luxburg; 10(Mar):657--698, 2009.
[abs][pdf]

Properties of Monotonic Effects on Directed Acyclic Graphs
Tyler J. VanderWeele, James M. Robins; 10(Mar):699--718, 2009.
[abs][pdf]

On Efficient Large Margin Semisupervised Learning: Method and Theory
Junhui Wang, Xiaotong Shen, Wei Pan; 10(Mar):719--742, 2009.
[abs][pdf]

Nieme: Large-Scale Energy-Based Models    (Machine Learning Open Source Software Paper)
Francis Maes; 10(Mar):743--746, 2009.
[abs][pdf]    [code][mloss.org]

Similarity-based Classification: Concepts and Algorithms
Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti; 10(Mar):747--776, 2009.
[abs][pdf]

Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang; 10(Mar):777--801, 2009.
[abs][pdf]

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-Philippe Vert; 10(Mar):803--826, 2009.
[abs][pdf]

Consistency and Localizability
Alon Zakai, Ya'acov Ritov; 10(Apr):827--856, 2009.
[abs][pdf]

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; 10(Apr):857--882, 2009.
[abs][pdf]

Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
Holger Höfling, Robert Tibshirani; 10(Apr):883--906, 2009.
[abs][pdf]

Polynomial-Delay Enumeration of Monotonic Graph Classes
Jan Ramon, Siegfried Nijssen; 10(Apr):907--929, 2009.
[abs][pdf]

Java-ML: A Machine Learning Library    (Machine Learning Open Source Software Paper)
Thomas Abeel, Yves Van de Peer, Yvan Saeys; 10(Apr):931--934, 2009.
[abs][pdf]    [code][mloss.org]

Nonextensive Information Theoretic Kernels on Measures
André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo; 10(Apr):935--975, 2009.
[abs][pdf]

On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality
Wenxin Jiang; 10(Apr):977--996, 2009.
[abs][pdf]

Fourier Theoretic Probabilistic Inference over Permutations
Jonathan Huang, Carlos Guestrin, Leonidas Guibas; 10(May):997--1070, 2009.
[abs][pdf]

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; 10(May):1071--1094, 2009.
[abs][pdf]

Universal Kernel-Based Learning with Applications to Regular Languages    (Special Topic on Mining and Learning with Graphs and Relations)
Leonid (Aryeh) Kontorovich, Boaz Nadler; 10(May):1095--1129, 2009.
[abs][pdf]

Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Hui Li, Xuejun Liao, Lawrence Carin; 10(May):1131--1186, 2009.
[abs][pdf]

The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
Ricardo Silva, Zoubin Ghahramani; 10(Jun):1187--1238, 2009.
[abs][pdf]

Incorporating Functional Knowledge in Neural Networks
Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia; 10(Jun):1239--1262, 2009.
[abs][pdf]

Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther, Manfred Opper; 10(Jun):1263--1304, 2009.
[abs][pdf]

Robust Process Discovery with Artificial Negative Events    (Special Topic on Mining and Learning with Graphs and Relations)
Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens; 10(Jun):1305--1340, 2009.
[abs][pdf]

Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination    (Special Topic on Model Selection)
Eugene Tuv, Alexander Borisov, George Runger, Kari Torkkola; 10(Jul):1341--1366, 2009.
[abs][pdf]

A Parameter-Free Classification Method for Large Scale Learning
Marc Boullé; 10(Jul):1367--1385, 2009.
[abs][pdf]

Model Monitor (M2): Evaluating, Comparing, and Monitoring Models    (Machine Learning Open Source Software Paper)
Troy Raeder, Nitesh V. Chawla; 10(Jul):1387--1390, 2009.
[abs][pdf]    [code][mloss.org]

A Least-squares Approach to Direct Importance Estimation
Takafumi Kanamori, Shohei Hido, Masashi Sugiyama; 10(Jul):1391--1445, 2009.
[abs][pdf]

Classification with Gaussians and Convex Loss
Dao-Hong Xiang, Ding-Xuan Zhou; 10(Jul):1447--1468, 2009.
[abs][pdf]

Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Jean Hausser, Korbinian Strimmer; 10(Jul):1469--1484, 2009.
[abs][pdf]

Robustness and Regularization of Support Vector Machines
Huan Xu, Constantine Caramanis, Shie Mannor; 10(Jul):1485--1510, 2009.
[abs][pdf]

Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras; 10(Jul):1511--1526, 2009.
[abs][pdf]

Bayesian Network Structure Learning by Recursive Autonomy Identification
Raanan Yehezkel, Boaz Lerner; 10(Jul):1527--1570, 2009.
[abs][pdf]

Learning Linear Ranking Functions for Beam Search with Application to Planning
Yuehua Xu, Alan Fern, Sungwook Yoon; 10(Jul):1571--1610, 2009.
[abs][pdf]

Marginal Likelihood Integrals for Mixtures of Independence Models
Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu; 10(Jul):1611--1631, 2009.
[abs][pdf]

Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew E. Taylor, Peter Stone; 10(Jul):1633--1685, 2009.
[abs][pdf]

Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Eitan Greenshtein, Junyong Park; 10(Jul):1687--1704, 2009.
[abs][pdf]

Learning Permutations with Exponential Weights
David P. Helmbold, Manfred K. Warmuth; 10(Jul):1705--1736, 2009.
[abs][pdf]

SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
Antoine Bordes, Léon Bottou, Patrick Gallinari; 10(Jul):1737--1754, 2009.
[abs][pdf]

Dlib-ml: A Machine Learning Toolkit    (Machine Learning Open Source Software Paper)
Davis E. King; 10(Jul):1755--1758, 2009.
[abs][pdf]    [code][mloss.org]

Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
Halbert White, Karim Chalak; 10(Aug):1759--1799, 2009.
[abs][pdf]

Distributed Algorithms for Topic Models
David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling; 10(Aug):1801--1828, 2009.
[abs][pdf]

Nonlinear Models Using Dirichlet Process Mixtures
Babak Shahbaba, Radford Neal; 10(Aug):1829--1850, 2009.
[abs][pdf]

CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
Roberto Esposito, Daniele P. Radicioni; 10(Aug):1851--1880, 2009.
[abs][pdf]

Learning Acyclic Probabilistic Circuits Using Test Paths
Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin; 10(Aug):1881--1911, 2009.
[abs][pdf]

Learning Approximate Sequential Patterns for Classification
Zeeshan Syed, Piotr Indyk, John Guttag; 10(Aug):1913--1936, 2009.
[abs][pdf]

Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
Kristian Woodsend, Jacek Gondzio; 10(Aug):1937--1953, 2009.
[abs][pdf]

Provably Efficient Learning with Typed Parametric Models
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy; 10(Aug):1955--1988, 2009.
[abs][pdf]

Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Jie Chen, Haw-ren Fang, Yousef Saad; 10(Sep):1989--2012, 2009.
[abs][pdf]

Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
Jianqing Fan, Richard Samworth, Yichao Wu; 10(Sep):2013--2038, 2009.
[abs][pdf]

Evolutionary Model Type Selection for Global Surrogate Modeling
Dirk Gorissen, Tom Dhaene, Filip De Turck; 10(Sep):2039--2078, 2009.
[abs][pdf]

An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
Luciana Ferrer, Kemal Sönmez, Elizabeth Shriberg; 10(Sep):2079--2114, 2009.
[abs][pdf]

Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
Christian Rieger, Barbara Zwicknagl; 10(Sep):2115--2132, 2009.
[abs][pdf]

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments    (Machine Learning Open Source Software Paper)
Brian Tanner, Adam White; 10(Sep):2133--2136, 2009.
[abs][pdf]    [code][mloss.org]

Discriminative Learning Under Covariate Shift
Steffen Bickel, Michael Brückner, Tobias Scheffer; 10(Sep):2137--2155, 2009.
[abs][pdf]

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
Vojtěch Franc, Sören Sonnenburg; 10(Oct):2157--2192, 2009.
[abs][pdf]

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Cynthia Rudin, Robert E. Schapire; 10(Oct):2193--2232, 2009.
[abs][pdf]

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
Cynthia Rudin; 10(Oct):2233--2271, 2009.
[abs][pdf]

Learning Nondeterministic Classifiers
Juan José del Coz, Jorge Díez, Antonio Bahamonde; 10(Oct):2273--2293, 2009.
[abs][pdf]

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Han Liu, John Lafferty, Larry Wasserman; 10(Oct):2295--2328, 2009.
[abs][pdf]

Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
Mathias Drton, Michael Eichler, Thomas S. Richardson; 10(Oct):2329--2348, 2009.
[abs][pdf]

Estimating Labels from Label Proportions
Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le; 10(Oct):2349--2374, 2009.
[abs][pdf]

Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page; 10(Oct):2375--2411, 2009.
[abs][pdf]

Reinforcement Learning in Finite MDPs: PAC Analysis
Alexander L. Strehl, Lihong Li, Michael L. Littman; 10(Nov):2413−2444, 2009.
[abs][pdf]

Prediction With Expert Advice For The Brier Game
Vladimir Vovk, Fedor Zhdanov; 10(Nov):2445−2471, 2009.
[abs][pdf]

Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
Saharon Rosset; 10(Nov):2473−2505, 2009.
[abs][pdf]

When Is There a Representer Theorem? Vector Versus Matrix Regularizers
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil; 10(Nov):2507−2529, 2009.
[abs][pdf]

Maximum Entropy Discrimination Markov Networks
Jun Zhu, Eric P. Xing; 10(Nov):2531−2569, 2009.
[abs][pdf]

Learning When Concepts Abound
Omid Madani, Michael Connor, Wiley Greiner; 10(Nov):2571−2613, 2009.
[abs][pdf]

Hash Kernels for Structured Data
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan; 10(Nov):2615−2637, 2009.
[abs][pdf]

DL-Learner: Learning Concepts in Description Logics
Jens Lehmann; 10(Nov):2639−2642, 2009.
[abs][pdf]    [code][mloss.org]

Bounded Kernel-Based Online Learning
Francesco Orabona, Joseph Keshet, Barbara Caputo; 10(Nov):2643−2666, 2009.
[abs][pdf]

Structure Spaces
Brijnesh J. Jain, Klaus Obermayer; 10(Nov):2667−2714, 2009.
[abs][pdf]

Learning Halfspaces with Malicious Noise
Adam R. Klivans, Philip M. Long, Rocco A. Servedio; 10(Dec):2715−2740, 2009.
[abs][pdf]

Reproducing Kernel Banach Spaces for Machine Learning
Haizhang Zhang, Yuesheng Xu, Jun Zhang; 10(Dec):2741−2775, 2009.
[abs][pdf]

Cautious Collective Classification
Luke K. McDowell, Kalyan Moy Gupta, David W. Aha; 10(Dec):2777−2836, 2009.
[abs][pdf]    [appendices]

Adaptive False Discovery Rate Control under Independence and Dependence
Gilles Blanchard, Étienne Roquain; 10(Dec):2837−2871, 2009.
[abs][pdf]

Online Learning with Samples Drawn from Non-identical Distributions
Ting Hu, Ding-Xuan Zhou; 10(Dec):2873−2898, 2009.
[abs][pdf]

Efficient Online and Batch Learning Using Forward Backward Splitting
John Duchi, Yoram Singer; 10(Dec):2899−2934, 2009.
[abs][pdf]

A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
Asela Gunawardana, Guy Shani; 10(Dec):2935−2962, 2009.
[abs][pdf]




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