13. COLT 2000: Palo Alto, California, USA

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

Session 1

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  David A. McAllester, Robert E. Schapire:
  On the Convergence Rate of Good-Turing Estimators. COLT 2000: 1-6
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  Jeffrey C. Jackson:
  On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries. COLT 2000: 7-15
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  Dinesh P. Mehta, Vijay Raghavan:
  Decision Tree Approximations of Boolean Functions. COLT 2000: 16-24
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  John Langford, David A. McAllester:
  Computable Shell Decomposition Bounds. COLT 2000: 25-34

Session 2

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  Koby Crammer, Yoram Singer:
  On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46
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  Sanjay Jain, Efim B. Kinber, Rolf Wiehagen:
  Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations. COLT 2000: 47-58
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  Frank Stephan, Thomas Zeugmann:
  Average-Case Complexity of Learning Polynomials. COLT 2000: 59-68

Session 3

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  Yishay Mansour, David A. McAllester:
  Generalization Bounds for Decision Trees. COLT 2000: 69-74
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  Nicolas Vayatis:
  The Role of Critical Sets in Vapnik-Chervonenkis Theory. COLT 2000: 75-80
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  Shahar Mendelson, Naftali Tishby:
  Statistical Sufficiency for Classes in Empirical L₂ Spaces. COLT 2000: 81-89

Session 4

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  Jürgen Forster, Manfred K. Warmuth:
  Relative Expected Instantaneous Loss Bounds. COLT 2000: 90-99
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  Eiji Takimoto, Manfred K. Warmuth:
  The Minimax Strategy for Gaussian Density Estimation. pp. COLT 2000: 100-106
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  Peter Auer, Claudio Gentile:
  Adaptive and Self-Confident On-Line Learning Algorithms. COLT 2000: 107-117
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  Peter Auer:
  An Improved On-line Algorithm for Learning Linear Evaluation Functions. COLT 2000: 118-125

Session 5

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  Yoav Freund, Manfred Opper:
  Continuous Drifting Games. COLT 2000: 126-132
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  Peter L. Bartlett, Jonathan Baxter:
  Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141
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  Michael J. Kearns, Satinder P. Singh:
  Bias-Variance Error Bounds for Temporal Difference Updates. COLT 2000: 142-147

Session 6

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  Rocco A. Servedio:
  PAC Analogues of Perceptron and Winnow via Boosting the Margin. COLT 2000: 148-157
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  Michael Collins, Robert E. Schapire, Yoram Singer:
  Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169
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  Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller:
  Barrier Boosting. COLT 2000: 170-179
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  Carlos Domingo, Osamu Watanabe:
  MadaBoost: A Modification of AdaBoost. COLT 2000: 180-189

Session 7

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  Ron Meir, Ran El-Yaniv, Shai Ben-David:
  Localized Boosting. COLT 2000: 190-199
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  Javed A. Aslam:
  Improving Algorithms for Boosting. COLT 2000: 200-207
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  Nigel Duffy, David P. Helmbold:
  Leveraging for Regression. COLT 2000: 208-219
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  Yishay Mansour, David A. McAllester:
  Boosting Using Branching Programs. COLT 2000: 220-224

Session 8

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  Paul W. Goldberg, Stephen Kwek:
  The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries. COLT 2000: 225-235
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  Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán:
  Improved Algorithms for Theory Revision with Queries. COLT 2000: 236-247
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  José L. Balcázar, Jorge Castro, David Guijarro:
  Abstract Combinatorial Characterizations of Exact Learning via Queries. COLT 2000: 248-254

Session 9

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  Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
  The Computational Complexity of Densest Region Detection. COLT 2000: 255-265
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  Shai Ben-David, Nadav Eiron, Philip M. Long:
  On the Difficulty of Approximately Maximizing Agreements. COLT 2000: 266-274
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  Christian Kuhlmann:
  Hardness Results for General Two-Layer Neural Networks. COLT 2000: 275-285

Session 10

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  Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
  Model Selection and Error Estimation. COLT 2000: 286-297
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  Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
  Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303
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  Ralf Herbrich, Thore Graepel, John Shawe-Taylor:
  Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308
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  Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
  Entropy Numbers of Linear Function Classes. COLT 2000: 309-319

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