Learning Theory 19th Annual Conference on Learning Theory, COLT 2006
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Subseries of Lecture Notes in Computer Science
4005
Gabor Lugosi Hans Ulrich Simon (Eds.)
Learning Theory 19th Annual Conference on Learning Theory, COLT 2006 Pittsburgh, PA, USA, June 22-25, 2006 Proceedings
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Gabor Lugosi Pompeu Fabra University Ramon Trias Fargas 25-27, 08005, Barcelona, Spain E-mail: [email protected] Hans Ulrich Simon Ruhr University Bochum, Department of Mathematics Building NA 1/73, 44780 Bochum, Germany E-mail: [email protected]
Library of Congress Control Number: 2006927286
CR Subject Classification (1998): I.2.6, I.2.3, I.2, F.4.1, F.2, F.1.1 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-35294-5 Springer Berlin Heidelberg New York 978-3-540-35294-5 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 11776420 06/3142 543210
Preface
This volume contains papers presented at the 19th Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held at the Carnegie Mellon University in Pittsburgh, USA, June 22–25, 2006. The technical program contained 43 papers selected from 102 submissions, 2 open problems selected from among 4 contributed, and 3 invited lectures. The invited lectures were given by Luc Devroye on “Random Multivariate Search Trees,” by Gy¨orgy Tur´ an on “Learning and Logic,” and by Vladimir Vovk on “Predictions as Statements and Decisions.” The abstracts of these papers are 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 three further awards funded by the Machine Learning Journal. We were therefore able to select four student papers for prizes. The students selected were Guillaume Lecu´e for the single-author paper “Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition,” Homin K. Lee and Andrew Wan for the paper “DNF are Teachable in the Average Case” (co-authored by Rocco A. Servedio), Alexander A. Sherstov for the paper “Improved Lower Bounds for Learning the Intersection
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