This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004.
The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.
This volume contains papers presented at the 17th Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in Banff, Canada from July 1 to 4, 2004.
The technical program contained 43 papers selected from 107 submissions, 3 open problems selected from among 6 contributed, and 3 invited lectures. The invited lectures were given by Michael Kearns on ‘Game Theory, Automated Trading and Social Networks’, Moses Charikar on ‘Algorithmic Aspects of Finite Metric Spaces’, and Stephen Boyd on ‘Convex Optimization, Semidefinite Programming, and Recent Applications’. These papers were not included in this volume.
The Mark Fulk Award is presented annually for the best paper co-authored by a student. This year the Mark Fulk award was supplemented with two further awards funded by the Machine Learning Journal and the National Information Communication Technology Centre, Australia (NICTA). We were therefore able to select three student papers for prizes. The students selected were Magalie Fromont for the single-author paper “Model Selection by Bootstrap Penalization for Classification”, Daniel Reidenbach for the single-author paper “On the Learnability of E-Pattern Languages over Small Alphabets”, and Ran Gilad-Bachrach for the paper “Bayes and Tukey Meet at the Center Point” (co-authored with Amir Navot and Naftali Tishby).
