Algorithmic Learning Theory

23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings

  • Nader H. Bshouty
  • Gilles Stoltz
  • Nicolas Vayatis
  • Thomas Zeugmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7568)

Table of contents

  1. Front Matter
  2. Editors’ Introduction

    1. Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
      Pages 1-11
  3. Invited Papers

    1. Shai Shalev-Shwartz
      Pages 13-16
    2. Pascal Massart, Caroline Meynet
      Pages 17-33
    3. Toon Calders
      Pages 34-34
    4. Gilbert Ritschard
      Pages 35-35
  4. Regular Contributions

    1. Inductive Inference

      1. Sanjay Jain, Timo Kötzing, Frank Stephan
        Pages 36-50
      2. Christophe Costa Florêncio, Sicco Verwer
        Pages 81-95
    2. Teaching and PAC-Learning

      1. Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles
        Pages 96-110
      2. Dana Angluin, James Aspnes, Aryeh Kontorovich
        Pages 111-123
    3. Statistical Learning Theory and Classification

      1. Mehryar Mohri, Andres Muñoz Medina
        Pages 124-138
      2. Hal Daumé III, Jeff M. Phillips, Avishek Saha, Suresh Venkatasubramanian
        Pages 154-168
    4. Relations between Models and Data

      1. Peter Grünwald
        Pages 169-183
    5. Bandit Problems

      1. Emilie Kaufmann, Nathaniel Korda, Rémi Munos
        Pages 199-213
      2. Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos
        Pages 214-228

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

Keywords

computational finance domain adaptation pattern mining reinforcement learning statistical learning theory

Editors and affiliations

  • Nader H. Bshouty
    • 1
  • Gilles Stoltz
    • 2
  • Nicolas Vayatis
    • 3
  • Thomas Zeugmann
    • 4
  1. 1.Department of Computer ScienceTechnionHaifaIsrael
  2. 2.Ecolre Normale Sup’erieure, CNRS, INRIAParisFrance
  3. 3.Ecole Normale Supérieure de CachanCachan cedexFrance
  4. 4.Division of Computer ScienceHokkaido UniversitySapporoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-34106-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-34105-2
  • Online ISBN 978-3-642-34106-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book