COLT 2000

35 papers

Abstract Combinatorial Characterizations of Exact Learning via Queries José L. Balcázar, Jorge Castro, David Guijarro

Adaptive and Self-Confident On-Line Learning Algorithms Peter Auer, Claudio Gentile

An Improved On-Line Algorithm for Learning Linear Evaluation Functions Peter Auer

Average-Case Complexity of Learning Polynomials Frank Stephan, Thomas Zeugmann

Barrier Boosting Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller

Bias-Variance Error Bounds for Temporal Difference Updates Michael J. Kearns, Satinder Singh

Boosting Using Branching Programs Yishay Mansour, David A. McAllester

Computable Shell Decomposition Bounds John Langford, David A. McAllester

Continuous Drifting Games Yoav Freund, Manfred Opper

Decision Tree Approximations of Boolean Functions Dinesh P. Mehta, Vijay Raghavan

Entropy Numbers of Linear Function Classes Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning Peter L. Bartlett, Jonathan Baxter

Generalisation Error Bounds for Sparse Linear Classifiers Thore Graepel, Ralf Herbrich, John Shawe-Taylor

Generalization Bounds for Decision Trees Yishay Mansour, David A. McAllester

Hardness Results for General Two-Layer Neural Networks Christian Kuhlmann

Improved Algorithms for Theory Revision with Queries Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán

Improving Algorithms for Boosting Javed A. Aslam

Language Learning from Texts: Degrees of Instrinsic Complexity and Their Characterizations Sanjay Jain, Efim B. Kinber, Rolf Wiehagen

Leveraging for Regression Nigel Duffy, David P. Helmbold

Localized Boosting Ron Meir, Ran El-Yaniv, Shai Ben-David

Logistic Regression, AdaBoost and Bregman Distances Michael Collins, Robert E. Schapire, Yoram Singer

MadaBoost: A Modification of AdaBoost Carlos Domingo, Osamu Watanabe

Model Selection and Error Estimation Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi

On the Convergence Rate of Good-Turing Estimators David A. McAllester, Robert E. Schapire

On the Difficulty of Approximately Maximizing Agreements Shai Ben-David, Nadav Eiron, Philip M. Long

On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries Jeffrey C. Jackson

On the Learnability and Design of Output Codes for Multiclass Problems Koby Crammer, Yoram Singer

PAC Analogues of Perceptron and Winnow via Boosting the Margin Rocco A. Servedio

Relative Expected Instantaneous Loss Bounds Jürgen Forster, Manfred K. Warmuth

Sparsity vs. Large Margins for Linear Classifiers Ralf Herbrich, Thore Graepel, John Shawe-Taylor

Statistical Sufficiency for Classes in Empirical L2 Spaces Shahar Mendelson, Naftali Tishby

The Computational Complexity of Densest Region Detection Shai Ben-David, Nadav Eiron, Hans Ulrich Simon

The Minimax Strategy for Gaussian Density Estimation. Pp Eiji Takimoto, Manfred K. Warmuth

The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries Paul W. Goldberg, Stephen Kwek

The Role of Critical Sets in Vapnik-Chervonenkis Theory Nicolas Vayatis