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Algorithmic Learning Theory

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

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Peter Auer

    1. Montanuniversitaet Leoben, Leoben, Austria

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  2. Alexander Clark

    1. Department of Philosophy, King’s College, London, UK

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  3. Thomas Zeugmann

    1. Division of Computer Science, Hokkaido University, Sapporo, Japan

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  4. Sandra Zilles

    1. Department of Computer Science, University of Regina, Regina, Canada

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ALT 2014.

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Table of contents (24 papers)

  1. Front Matter

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  2. Editors’ Introduction

    • Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
    Pages 1-7

  3. Full Invited Papers

    1. Cellular Tree Classifiers

      • Gérard Biau, Luc Devroye
      Pages 8-17

    2. A Survey of Preference-Based Online Learning with Bandit Algorithms

      • Róbert Busa-Fekete, Eyke Hüllermeier
      Pages 18-39

  4. Regular Contributions

    1. Inductive Inference

      1. A Map of Update Constraints in Inductive Inference
        • Timo Kötzing, Raphaela Palenta
        Pages 40-54
      2. On the Role of Update Constraints and Text-Types in Iterative Learning
        • Sanjay Jain, Timo Kötzing, Junqi Ma, Frank Stephan
        Pages 55-69
      3. Parallel Learning of Automatic Classes of Languages
        • Sanjay Jain, Efim Kinber
        Pages 70-84
      4. Algorithmic Identification of Probabilities Is Hard
        • Laurent Bienvenu, Benoît Monin, Alexander Shen
        Pages 85-95

    2. Exact Learning from Queries

      1. Learning Boolean Halfspaces with Small Weights from Membership Queries
        • Hasan Abasi, Ali Z. Abdi, Nader H. Bshouty
        Pages 96-110
      2. On Exact Learning Monotone DNF from Membership Queries
        • Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi
        Pages 111-124
      3. Learning Regular Omega Languages
        • Dana Angluin, Dana Fisman
        Pages 125-139

    3. Reinforcement Learning

      1. Selecting Near-Optimal Approximate State Representations in Reinforcement Learning
        • Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko
        Pages 140-154
      2. Policy Gradients for CVaR-Constrained MDPs
        • L. A. Prashanth
        Pages 155-169
      3. Bayesian Reinforcement Learning with Exploration
        • Tor Lattimore, Marcus Hutter
        Pages 170-184
      4. Extreme State Aggregation beyond MDPs
        • Marcus Hutter
        Pages 185-199

    4. Online Learning and Learning with Bandit Information

      1. On Learning the Optimal Waiting Time
        • Tor Lattimore, András György, Csaba Szepesvári
        Pages 200-214
      2. Bandit Online Optimization over the Permutahedron
        • Nir Ailon, Kohei Hatano, Eiji Takimoto
        Pages 215-229
      3. Offline to Online Conversion
        • Marcus Hutter
        Pages 230-244

    5. Statistical Learning Theory

      1. A Chain Rule for the Expected Suprema of Gaussian Processes
        • Andreas Maurer
        Pages 245-259
      2. Generalization Bounds for Time Series Prediction with Non-stationary Processes
        • Vitaly Kuznetsov, Mehryar Mohri
        Pages 260-274
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About this book

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.

Editors and Affiliations

  • Montanuniversitaet Leoben, Leoben, Austria

    Peter Auer

  • Department of Philosophy, King’s College, London, UK

    Alexander Clark

  • Division of Computer Science, Hokkaido University, Sapporo, Japan

    Thomas Zeugmann

  • Department of Computer Science, University of Regina, Regina, Canada

    Sandra Zilles

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