Algorithmic Learning Theory

27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings

  • Ronald Ortner
  • Hans Ulrich Simon
  • Sandra Zilles
Conference proceedings ALT 2016

DOI: 10.1007/978-3-319-46379-7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9925)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Error Bounds, Sample Compression Schemes

    1. Front Matter
      Pages 1-1
    2. Nikita Zhivotovskiy, Steve Hanneke
      Pages 18-33
    3. Shay Moran, Manfred K. Warmuth
      Pages 34-49
    4. Shai Ben-David, Ruth Urner
      Pages 50-64
  3. Statistical Learning Theory, Evolvability

    1. Front Matter
      Pages 65-65
    2. Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
      Pages 67-82
  4. Exact and Interactive Learning, Complexity of Teaching Models

    1. Front Matter
      Pages 113-113
    2. Nader H. Bshouty, Areej Costa
      Pages 115-129
    3. Achilles A. Beros, Ziyuan Gao, Sandra Zilles
      Pages 145-160
  5. Inductive Inference

    1. Front Matter
      Pages 161-161
    2. Sanjay Jain, Efim Kinber
      Pages 174-188
    3. Rupert Hölzl, Sanjay Jain, Frank Stephan
      Pages 189-203
  6. Online Learning

    1. Front Matter
      Pages 205-205
    2. Nader H. Bshouty, Catherine A. Haddad-Zaknoon
      Pages 207-222

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

Keywords

active learning inductive inference online learning algorithms reinforcement learning sequential decision making adversary models boolean function learning clustering evolutionary algorithms interactive learning local search models of learning online learning theory optimization perceptron query learning sample complexity and generalization bounds structured prediction semi-supervised learning structured prediction unsupervised learning

Editors and affiliations

  • Ronald Ortner
    • 1
  • Hans Ulrich Simon
    • 2
  • Sandra Zilles
    • 3
  1. 1.Montanuniversität Leoben LeobenAustria
  2. 2.Ruhr-Uni-Bochum BochumGermany
  3. 3.University of Regina ReginaCanada

Bibliographic information

  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-46378-0
  • Online ISBN 978-3-319-46379-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349