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

About these proceedings


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.


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
  • About this book