© 2014

Algorithmic Learning Theory

25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings

  • Peter Auer
  • Alexander Clark
  • Thomas Zeugmann
  • Sandra Zilles
Conference proceedings ALT 2014

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8776)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 8776)

Table of contents

  1. Front Matter
  2. Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
    Pages 1-7
  3. Full Invited Papers

    1. Gérard Biau, Luc Devroye
      Pages 8-17
    2. Róbert Busa-Fekete, Eyke Hüllermeier
      Pages 18-39
  4. Regular Contributions

    1. Inductive Inference

      1. Timo Kötzing, Raphaela Palenta
        Pages 40-54
      2. Sanjay Jain, Timo Kötzing, Junqi Ma, Frank Stephan
        Pages 55-69
      3. Sanjay Jain, Efim Kinber
        Pages 70-84
      4. Laurent Bienvenu, Benoît Monin, Alexander Shen
        Pages 85-95
    2. Exact Learning from Queries

      1. Hasan Abasi, Ali Z. Abdi, Nader H. Bshouty
        Pages 96-110
      2. Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi
        Pages 111-124
      3. Dana Angluin, Dana Fisman
        Pages 125-139
    3. Reinforcement Learning

      1. Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko
        Pages 140-154
      2. L. A. Prashanth
        Pages 155-169
      3. Tor Lattimore, Marcus Hutter
        Pages 170-184
      4. Marcus Hutter
        Pages 185-199
    4. Online Learning and Learning with Bandit Information

      1. Tor Lattimore, András György, Csaba Szepesvári
        Pages 200-214
      2. Nir Ailon, Kohei Hatano, Eiji Takimoto
        Pages 215-229
      3. Marcus Hutter
        Pages 230-244
    5. Statistical Learning Theory

About these proceedings


This book constitutes the proceedings of the 25th International Conference on Algorithmic Learning Theory, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the 17th International Conference on Discovery Science, DS 2014. The 21 papers presented in this volume were carefully reviewed and selected from 50 submissions. In addition the book contains 4 full papers summarizing the invited talks. The papers are organized in topical sections named: inductive inference; exact learning from queries; reinforcement learning; online learning and learning with bandit information; statistical learning theory; privacy, clustering, MDL, and Kolmogorov complexity.


Kolmogorov complexity algorithmic learning theory artificial intelligence bandit theory computational complexity inductive inference machine learning theory online learning query learning reinforcemant learning semi-supervised learning statistical learning theory theory and algorithms for application domanins theory of computation

Editors and affiliations

  • Peter Auer
    • 1
  • Alexander Clark
    • 2
  • Thomas Zeugmann
    • 3
  • Sandra Zilles
    • 4
  1. 1.Montanuniversitaet LeobenLeobenAustria
  2. 2.Department of PhilosophyKing’s CollegeLondonUK
  3. 3.Division of Computer ScienceHokkaido UniversitySapporoJapan
  4. 4.Department of Computer ScienceUniversity of ReginaReginaCanada

Bibliographic information