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Learning Theory

18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005. Proceedings

  • Peter Auer
  • Ron Meir
Conference proceedings COLT 2005

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3559)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3559)

Table of contents

  1. Front Matter
  2. Learning to Rank

    1. Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis
      Pages 1-15
    2. Shivani Agarwal, Dan Roth
      Pages 16-31
    3. Shivani Agarwal, Partha Niyogi
      Pages 32-47
    4. Koby Crammer, Yoram Singer
      Pages 48-62
  3. Boosting

    1. Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire
      Pages 63-78
    2. Philip M. Long, Rocco A. Servedio
      Pages 79-94
  4. Unlabeled Data, Multiclass Classification

    1. Maria-Florina Balcan, Avrim Blum
      Pages 111-126
    2. Matti Kääriäinen
      Pages 127-142
    3. Ambuj Tewari, Peter L. Bartlett
      Pages 143-157
    4. John Langford, Alina Beygelzimer
      Pages 158-172
  5. Online Learning I

    1. Yuri Kalnishkan, Michael V. Vyugin
      Pages 188-203
    2. András György, Tamás Linder, Gábor Lugosi
      Pages 204-216
    3. Nicolò Cesa-Bianchi, Yishay Mansour, Gilles Stoltz
      Pages 217-232
  6. Online Learning II

    1. Baruch Awerbuch, Robert D. Kleinberg
      Pages 233-248
    2. Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni
      Pages 249-263
    3. Shai Shalev-Shwartz, Yoram Singer
      Pages 264-278
  7. Support Vector Machines

    1. Ingo Steinwart, Clint Scovel
      Pages 279-294
    2. Vladimir Koltchinskii, Olexandra Beznosova
      Pages 295-307
    3. Nikolas List, Hans Ulrich Simon
      Pages 308-322
  8. Kernels and Embeddings

    1. Petros Drineas, Michael W. Mahoney
      Pages 323-337
    2. Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
      Pages 338-352
    3. Shahar Mendelson
      Pages 353-365
    4. Manfred K. Warmuth, S. V. N. Vishwanathan
      Pages 366-381
  9. Inductive Inference

    1. Lorenzo Carlucci, Sanjay Jain, Efim Kinber, Frank Stephan
      Pages 382-397
    2. Wei Luo, Oliver Schulte
      Pages 398-412
  10. Unsupervised Learning

    1. S. Mendelson, A. Pajor
      Pages 429-443
    2. Ravindran Kannan, Hadi Salmasian, Santosh Vempala
      Pages 444-457
    3. Dimitris Achlioptas, Frank McSherry
      Pages 458-469
    4. Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
      Pages 470-485
    5. Mikhail Belkin, Partha Niyogi
      Pages 486-500
  11. Generalization Bounds

    1. Polina Golland, Feng Liang, Sayan Mukherjee, Dmitry Panchenko
      Pages 501-515
    2. Nathan Srebro, Adi Shraibman
      Pages 545-560
  12. Query Learning, Attribute Efficiency, Compression Schemes

    1. Dana Angluin, Jiang Chen
      Pages 561-575
    2. Dima Kuzmin, Manfred K. Warmuth
      Pages 591-605
  13. Economics and Game Theory

    1. Sham M. Kakade, Michael Kearns
      Pages 606-620
    2. Avrim Blum, Yishay Mansour
      Pages 621-636
  14. Separation Results for Learning Models

    1. Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan
      Pages 637-651
    2. Peter Grünwald, Steven de Rooij
      Pages 652-667
  15. Open Problems

    1. Dima Kuzmin, Manfred K. Warmuth
      Pages 684-686
    2. John Langford
      Pages 687-688
    3. Wei Luo
      Pages 689-690
  16. Back Matter

About these proceedings

Introduction

This volume contains papers presented at the Eighteenth Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained 45 papers selected from 120 submissions, 3 open problems selected from among 5 contributed, and 2 invited lectures. The invited lectures were given by Sergiu Hart on “Uncoupled Dynamics and Nash Equilibrium”, and by Satinder Singh on “Rethinking State, Action, and Reward in Reinforcement Learning”. These papers were not 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 Hadi Salmasian for the paper titled “The Spectral Method for General Mixture Models” co-authored with Ravindran Kannan and Santosh Vempala. The number of papers submitted to COLT this year was exceptionally high. In addition to the classical COLT topics, we found an increase in the number of submissions related to novel classi?cation scenarios such as ranking. This - crease re?ects a healthy shift towards more structured classi?cation problems, which are becoming increasingly relevant to practitioners.

Keywords

Boosting Support Vector Machine classification game theory learning learning theory supervised learning unsupervised learning

Editors and affiliations

  • Peter Auer
    • 1
  • Ron Meir
    • 2
  1. 1.University of LeobenLeobenAustria
  2. 2.Department of Electrical EngineeringTechnionHaifaIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/b137542
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-26556-6
  • Online ISBN 978-3-540-31892-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site