Algorithmic Learning Theory

22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings

  • Jyrki Kivinen
  • Csaba Szepesvári
  • Esko Ukkonen
  • Thomas Zeugmann

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6925)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 6925)

Table of contents

  1. Front Matter
  2. Editors’ Introduction

    1. Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann
      Pages 1-13
  3. Invited Papers

    1. Peter Auer, Shiau Hong Lim, Chris Watkins
      Pages 14-17
    2. Yoshua Bengio, Olivier Delalleau
      Pages 18-36
    3. Jorma Rissanen
      Pages 37-37
    4. Eyke Hüllermeier, Johannes Fürnkranz
      Pages 38-38
  4. Inductive Inference

    1. Sanjay Jain, Eric Martin, Frank Stephan
      Pages 55-69
    2. Sanjay Jain, Eric Martin, Frank Stephan
      Pages 70-83
    3. Michael Geilke, Sandra Zilles
      Pages 84-98
  5. Regression

    1. Sébastien Gerchinovitz, Jia Yuan Yu
      Pages 99-113
    2. Arnak S. Dalalyan, Joseph Salmon
      Pages 129-143
  6. Bandit Problems

    1. Sébastien Bubeck, Gilles Stoltz, Jia Yuan Yu
      Pages 144-158
    2. Antoine Salomon, Jean-Yves Audibert
      Pages 159-173
    3. Aurélien Garivier, Eric Moulines
      Pages 174-188
    4. Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer
      Pages 189-203
  7. Online Learning

    1. Constantinos Panagiotakopoulos, Petroula Tsampouka
      Pages 204-218
    2. Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
      Pages 219-233

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011.
The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models.

Keywords

algorithmic information theory game theory probability reinforcement learning universal artificial intelligence

Editors and affiliations

  • Jyrki Kivinen
    • 1
  • Csaba Szepesvári
    • 2
  • Esko Ukkonen
    • 1
  • Thomas Zeugmann
    • 3
  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Division of Computer ScienceHokkaido UniversitySapporoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-24412-4
  • Copyright Information Springer-Verlag GmbH Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-24411-7
  • Online ISBN 978-3-642-24412-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book