Algorithmic Learning Theory

24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings

  • Sanjay Jain
  • Rémi Munos
  • Frank Stephan
  • Thomas Zeugmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8139)

Table of contents

  1. Front Matter
  2. Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
    Pages 1-12
  3. Full Invited Papers

  4. Regular Contributions

    1. Online Learning

      1. Takahiro Fujita, Kohei Hatano, Eiji Takimoto
        Pages 68-82
      2. Jara Uitto, Roger Wattenhofer
        Pages 83-97
      3. Jiazhong Nie, Wojciech Kotłowski, Manfred K. Warmuth
        Pages 98-112
    2. Inductive Inference and Grammatical Inference

      1. Ziyuan Gao, Frank Stephan, Sandra Zilles
        Pages 113-127
      2. John Case, Timo Kötzing
        Pages 128-142
      3. Laurent Orseau, Tor Lattimore, Marcus Hutter
        Pages 158-172
    3. Teaching and Learning from Queries

      1. Malte Darnstädt, Thorsten Doliwa, Hans Ulrich Simon, Sandra Zilles
        Pages 173-187
      2. M. Jagadish, Anindya Sen
        Pages 188-202
    4. Bandit Theory

      1. Po-Ling Loh, Sebastian Nowozin
        Pages 203-217
      2. Odalric-Ambrym Maillard
        Pages 218-233
      3. Gergely Neu, Gábor Bartók
        Pages 234-248
    5. Statistical Learning Theory

      1. Anna Choromanska, Krzysztof Choromanski, Geetha Jagannathan, Claire Monteleoni
        Pages 249-263

About these proceedings

Introduction

This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning.

Keywords

combinatorial optimization gradient algorithms online learning reinforcement learning universal artificial intelligence

Editors and affiliations

  • Sanjay Jain
    • 1
  • Rémi Munos
    • 2
  • Frank Stephan
    • 1
  • Thomas Zeugmann
    • 3
  1. 1.National University of SingaporeRepublic of Singapore
  2. 2.Inria Lille - Nord Europe, Villeneuve d’AscqFrance
  3. 3.Hokkaido UniversitySapporoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-40935-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-40934-9
  • Online ISBN 978-3-642-40935-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book