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

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

Entropy Numbers of Linear Function Classes
Robert C. Williamson, Alex J. Smola, and Bernhard Scholkopf

Improved Algorithms for Theory Revision with Queries
Judy Goldsmith and Robert H. Sloan and Balazs Szorenyi and Gyorgy Turan

Barrier Boosting
G. Raetsch and S. Mika and T. Onoda and S. Lemm and K.-R. Mueller

Decision Tree Approximations of Boolean Functions
D.Mehta and V.Raghavan

On the difficulty of approximately maximizing agreements
Shai Ben-David and Nadav Eiron and Philip M. Long

Abstract combinatorial characterizations of exact learning via queries
Jose Luis Balcazar and Jorge Casto and David Guijarro

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

MadaBoost: A modification of AdaBoost
Carlos Domingo and Osamu Watanabe

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

The Minimax Strategy for Gaussian Density Estimation
Eiji Takimoto and Manfred Warmuth

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

Generalization Bounds for Decision Trees
Yishay Mansour and David McAllester

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

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

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

Statistical Sufficiency for Classes in Empirical L_2 Spaces
Shahar Mendelson and Naftali Tishby

Continuous drifting games
Yoav Freund and Manfred Opper

Model Selection and Error Estimation
Peter L. Bartlett and Stephane Boucheron and Gabor Lugosi

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

Boosting using Branching Programs
Yishay Mansour and David McAllester

Bias-Variance Error Bounds for Temporal Difference Updates
Michael Kearns and Satinder Singh

Hardness Results for General Two-Layer Neural Networks
Christian Kuhlmann

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

Relative Expected Instantaneous Loss Bounds
Juergen Forster and Manfred Warmuth

Leveraging for Regression
Nigel Duffy and David Helmbold

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

Generalisation error bounds for sparse linear classifiers
Thore Graepel and Ralf Herbrich and John Shawe-Taylor

Sparsity vs. large margins for linear classifiers: A small sample study
Ralf Herbrich and Thore Graepel and John Shawe-Taylor

Computable Shell Decomposition bounds
John Langford and David McAllester

The Complexity of Densest Region Detection
Shai Ben-David and Nadav Eiron and Hans Simon

AdaBoost and Logistic Regression Unified in the Context of Information Geometry
Michael Collins and Robert E. Schapire and Yoram Singer

An improved algorithm for Boosting
Javed Aslam