13. COLT 2000: Palo Alto, California, USA

@proceedings{DBLP:conf/colt/2000,
  editor    = {Nicol{\`o} Cesa-Bianchi and
               Sally A. Goldman},
  title     = {Proceedings of the Thirteenth Annual Conference on Computational
               Learning Theory (COLT 2000), June 28 - July 1, 2000, Palo Alto,
               California},
  publisher = {Morgan Kaufmann},
  year      = {2000},
  isbn      = {1-55860-703-X},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Session 1

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

Session 2

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

Session 3

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

Session 4

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

Session 5

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

Session 6

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

Session 7

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

Session 8

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

Session 9

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

Session 10

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