Learning Theory 20th Annual Conference on Learning Theory, COLT 2007
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Subseries of Lecture Notes in Computer Science
4539
Nader H. Bshouty Claudio Gentile (Eds.)
Learning Theory 20th Annual Conference on Learning Theory, COLT 2007 San Diego, CA, USA, June 13-15, 2007 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 Nader H. Bshouty Department of Computer Science Technion, Haifa, 32000, Israel E-mail: [email protected] Claudio Gentile Dipartimento di Informatica e Comunicazione Università dell’Insubria, Varese, Italy E-mail: [email protected]
Library of Congress Control Number: 2007927819
CR Subject Classification (1998): I.2.6, I.2.3, I.2, F.4.1-2, F.2, F.1.1 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-72925-9 Springer Berlin Heidelberg New York 978-3-540-72925-9 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 2007 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12073375 06/3180 543210
Preface
This volume contains papers presented at the 20th Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in San Diego, USA, June 13-15, 2007, as part of the 2007 Federated Computing Research Conference (FCRC). The Technical Program contained 41 papers selected from 92 submissions, 5 open problems selected from among 7 contributed, and 2 invited lectures. The invited lectures were given by Dana Ron on “Property Testing: A Learning Theory Perspective,” and by Santosh Vempala on “Spectral Algorithms for Learning and Clustering.” The abstracts of these lectures are included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. The student selected this year was Samuel E. Moelius III for the paper “U-Shaped, Iterative, and Iterative-with-Counter Learning” co-authored with John Case. This year, student awards were also granted by the Machine Learning Journal. We have therefore been able to select two more student papers for prizes. The students selected were Lev Reyzin for the paper “Learning LargeAlphabet and Analog Circuits with Value Injection Queries” (co-authored with Dana Angluin, James Aspnes, and Jiang Chen), and Jennifer Wortma
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