International Conference on Algorithmic Learning Theory

Algorithmic Learning Theory

26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings

  • Kamalika Chaudhuri
  • CLAUDIO GENTILE
  • Sandra Zilles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9355)

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Invited Papers

    1. Front Matter
      Pages 1-1
    2. Kai Zhong, Prateek Jain, Inderjit S. Dhillon
      Pages 3-18
    3. Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky
      Pages 19-38
  3. Inductive Inference

    1. Front Matter
      Pages 39-39
    2. Sanjay Jain, Junqi Ma, Frank Stephan
      Pages 41-55
    3. Ziyuan Gao, Frank Stephan, Sandra Zilles
      Pages 56-70
  4. Learning from Queries, Teaching Complexity

    1. Front Matter
      Pages 71-71
    2. Montserrat Hermo, Ana Ozaki
      Pages 73-88
    3. Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi
      Pages 89-101
    4. Ziyuan Gao, Hans Ulrich Simon, Sandra Zilles
      Pages 102-116
  5. Computational Learning Theory and Algorithms

    1. Front Matter
      Pages 117-117
    2. Malte Darnstädt, Christoph Ries, Hans Ulrich Simon
      Pages 134-148
    3. Steve Hanneke, Varun Kanade, Liu Yang
      Pages 149-164
  6. Statistical Learning Theory and Sample Complexity

    1. Front Matter
      Pages 177-177
    2. Borja Balle, Mehryar Mohri
      Pages 179-193
    3. Anastasia Pentina, Shai Ben-David
      Pages 194-208
    4. Ilya Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
      Pages 209-223

About these proceedings

Introduction

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Keywords

Active learning Boolean function learning Inductive inference Machine learning theory Markov Decision Processes Models of learning Online learning theory Query learning Regret bounds Reinforcement learning Unsupervised learning sample complexity generalization bounds algorithmic learning theory computational complexity inductive inference Kolmogorov complexity semi-supervised learning statistical learning theory

Editors and affiliations

  • Kamalika Chaudhuri
    • 1
  • CLAUDIO GENTILE
    • 2
  • Sandra Zilles
    • 3
  1. 1.University of CaliforniaLa JollaUSA
  2. 2.UNIV DEL INSUBRIA21100 VARESEItaly
  3. 3.REGINACanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-24486-0
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-24485-3
  • Online ISBN 978-3-319-24486-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book