Volume 3, July 2002

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  Francis R. Bach, Michael I. Jordan: Kernel Independent Component Analysis. 1-48
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  Nader H. Bshouty, Nadav Eiron: Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries. 49-57
• 
  John N. Tsitsiklis: On the Convergence of Optimistic Policy Iteration. 59-72
• 
  András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi: Data-dependent margin-based generalization bounds for classification. 73-98

Volume 3, August 2002

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  Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski: Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. 99-114
• 
  Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber: Learning Precise Timing with LSTM Recurrent Networks. 115-143
• 
  Istvan Szita, Bálint Takács, András Lörincz: MDPs: Learning in Varying Environments. 145-174

Volume 3, September 2002

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  Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. 175-212

Volume 3, October 2002

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  Ronen I. Brafman, Moshe Tennenholtz: R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning. 213-231
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  Matthias Seeger: PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. 233-269
• 
  Ziv Nevo, Ran El-Yaniv: On Online Learning of Decision Lists. 271-301

Volume 3, November 2002

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  Glenn Fung, Olvi L. Mangasarian, Alex J. Smola: Minimal Kernel Classifiers. 303-321
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  Masashi Sugiyama, Klaus-Robert Müller: The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. 323-359

Special Issue on Computational Learning Theory

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  Olivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. 363-396
• 
  Peter Auer: Using Confidence Bounds for Exploitation-Exploration Trade-offs. 397-422
• 
  Adam Kalai, Santosh Vempala: Efficient Algorithms for Universal Portfolios. 423-440
• 
  Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon: Limitations of Learning Via Embeddings in Euclidean Half Spaces. 441-461
• 
  Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. 463-482
• 
  Nader H. Bshouty, Dmitry Gavinsky: On Boosting with Polynomially Bounded Distributions. 483-506
• 
  David Maxwell Chickering: Optimal Structure Identification With Greedy Search. 507-554

Volume 3, December 2002

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  Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan: A Robust Minimax Approach to Classification. 555-582
• 
  Alexander Strehl, Joydeep Ghosh: Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions. 583-617

Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001)

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  Hendrik Blockeel, Jan Struyf: Efficient Algorithms for Decision Tree Cross-validation. 621-650
• 
  Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar: Multiple-Instance Learning of Real-Valued Data. 651-678
• 
  Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar: Learning Probabilistic Models of Link Structure. 679-707
• 
  Charles X. Ling, Huajie Zhang: The Representational Power of Discrete Bayesian Networks. 709-721
• 
  Mario Marchand, John Shawe-Taylor: The Set Covering Machine. 723-746
• 
  Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir: Coupled Clustering: A Method for Detecting Structural Correspondence. 747-780
• 
  Prasanth B. Nair, Arindam Choudhury, Andy J. Keane: Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels. 781-801
• 
  Theodore J. Perkins, Andrew G. Barto: Lyapunov Design for Safe Reinforcement Learning. 803-832
• 
  Tobias Scheffer, Stefan Wrobel: Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. 833-862
• 
  Marc Sebban, Richard Nock, Stéphane Lallich: Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. 863-885
• 
  Bryan Singer, Manuela M. Veloso: Learning to Construct Fast Signal Processing Implementations. 887-919
• 
  Malcolm J. A. Strens, Andrew W. Moore: Policy Search using Paired Comparisons. 921-950

Volume 3, January 2003

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  Koby Crammer, Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. 951-991
• 
  David M. Blei, Andrew Y. Ng, Michael I. Jordan: Latent Dirichlet Allocation. 993-1022

Volume 3, Febuary 2003

Special Issue on Machine Learning Methods for Text and Images

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  Koby Crammer, Yoram Singer: A Family of Additive Online Algorithms for Category Ranking. 1025-1058
• 
  Nicola Cancedda, Éric Gaussier, Cyril Goutte, Jean-Michel Renders: Word-Sequence Kernels. 1059-1082
• 
  Dmitry Zelenko, Chinatsu Aone, Anthony Richardella: Kernel Methods for Relation Extraction. 1083-1106
• 
  Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan: Matching Words and Pictures. 1107-1135
• 
  Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Janvin: A Neural Probabilistic Language Model. 1137-1155

Volume 3, March 2003

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  Isabelle Guyon, André Elisseeff: An Introduction to Variable and Feature Selection. 1157-1182
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  Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter: Distributional Word Clusters vs. Words for Text Categorization. 1183-1208
• 
  Yoshua Bengio, Nicolas Chapados: Extensions to Metric-Based Model Selection. 1209-1227
• 
  Jinbo Bi, Kristin P. Bennett, Mark J. Embrechts, Curt M. Breneman, Minghu Song: Dimensionality Reduction via Sparse Support Vector Machines. 1229-1243
• 
  Rich Caruana, Virginia R. de Sa: Benefitting from the Variables that Variable Selection Discards. 1245-1264
• 
  Inderjit S. Dhillon, Subramanyam Mallela, Rahul Kumar: A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification. 1265-1287
• 
  George Forman: An Extensive Empirical Study of Feature Selection Metrics for Text Classification. 1289-1305
• 
  Amir Globerson, Naftali Tishby: Sufficient Dimensionality Reduction. 1307-1331
• 
  Simon Perkins, Kevin Lacker, James Theiler: Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space. 1333-1356
• 
  Alain Rakotomamonjy: Variable Selection Using SVM-based Criteria. 1357-1370
• 
  Juha Reunanen: Overfitting in Making Comparisons Between Variable Selection Methods. 1371-1382
• 
  Isabelle Rivals, Léon Personnaz: MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling. 1383-1398
• 
  Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar: Ranking a Random Feature for Variable and Feature Selection. 1399-1414
• 
  Kari Torkkola: Feature Extraction by Non-Parametric Mutual Information Maximization. 1415-1438
• 
  Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping: Use of the Zero-Norm with Linear Models and Kernel Methods. 1439-1461