13. COLT 2000: Palo Alto, California, USA

Nicolò Cesa-Bianchi, Sally A. Goldman (Eds.): Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28 - July 1, 2000, Palo Alto, California. Morgan Kaufmann 2000, ISBN 1-55860-703-X

Session 1

David A. McAllester, Robert E. Schapire: On the Convergence Rate of Good-Turing Estimators. 1-6
Jeffrey C. Jackson: On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries. 7-15
Dinesh P. Mehta, Vijay Raghavan: Decision Tree Approximations of Boolean Functions. 16-24
John Langford, David A. McAllester: Computable Shell Decomposition Bounds. 25-34

Session 2

Koby Crammer, Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. 35-46
Sanjay Jain, Efim B. Kinber, Rolf Wiehagen: Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations. 47-58
Frank Stephan, Thomas Zeugmann: Average-Case Complexity of Learning Polynomials. 59-68

Session 3

Yishay Mansour, David A. McAllester: Generalization Bounds for Decision Trees. 69-74
Nicolas Vayatis: The Role of Critical Sets in Vapnik-Chervonenkis Theory. 75-80
Shahar Mendelson, Naftali Tishby: Statistical Sufficiency for Classes in Empirical L₂ Spaces. 81-89

Session 4

Jürgen Forster, Manfred K. Warmuth: Relative Expected Instantaneous Loss Bounds. 90-99
Eiji Takimoto, Manfred K. Warmuth: The Minimax Strategy for Gaussian Density Estimation. pp. 100-106
Peter Auer, Claudio Gentile: Adaptive and Self-Confident On-Line Learning Algorithms. 107-117
Peter Auer: An Improved On-line Algorithm for Learning Linear Evaluation Functions. 118-125

Session 5

Yoav Freund, Manfred Opper: Continuous Drifting Games. 126-132
Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. 133-141
Michael J. Kearns, Satinder P. Singh: Bias-Variance Error Bounds for Temporal Difference Updates. 142-147

Session 6

Rocco A. Servedio: PAC Analogues of Perceptron and Winnow via Boosting the Margin. 148-157
Michael Collins, Robert E. Schapire, Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. 158-169
Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller: Barrier Boosting. 170-179
Carlos Domingo, Osamu Watanabe: MadaBoost: A Modification of AdaBoost. 180-189

Session 7

Ron Meir, Ran El-Yaniv, Shai Ben-David: Localized Boosting. 190-199
Javed A. Aslam: Improving Algorithms for Boosting. 200-207
Nigel Duffy, David P. Helmbold: Leveraging for Regression. 208-219
Yishay Mansour, David A. McAllester: Boosting Using Branching Programs. 220-224

Session 8

Paul W. Goldberg, Stephen Kwek: The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries. 225-235
Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán: Improved Algorithms for Theory Revision with Queries. 236-247
José L. Balcázar, Jorge Castro, David Guijarro: Abstract Combinatorial Characterizations of Exact Learning via Queries. 248-254

Session 9

Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon: The Computational Complexity of Densest Region Detection. 255-265
Shai Ben-David, Nadav Eiron, Philip M. Long: On the Difficulty of Approximately Maximizing Agreements. 266-274
Christian Kuhlmann: Hardness Results for General Two-Layer Neural Networks. 275-285

Session 10

Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. 286-297
Thore Graepel, Ralf Herbrich, John Shawe-Taylor: Generalisation Error Bounds for Sparse Linear Classifiers. 298-303
Ralf Herbrich, Thore Graepel, John Shawe-Taylor: Sparsity vs. Large Margins for Linear Classifiers. 304-308
Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf: Entropy Numbers of Linear Function Classes. 309-319

Acknowledgement: The information on this page was contributed by Rocco A. Servedio

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