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Algorithmic Learning Theory

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

  • Conference proceedings
  • © 2012

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Overview

Editors:
  1. Nader H. Bshouty

    1. Department of Computer Science, Technion, Haifa, Israel

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  2. Gilles Stoltz

    1. Ecolre Normale Sup’erieure, CNRS, INRIA, Paris, France

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  3. Nicolas Vayatis

    1. Ecole Normale Supérieure de Cachan, Cachan cedex, France

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  4. Thomas Zeugmann

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7568)

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

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Conference proceedings info: ALT 2012.

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About this book

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.

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Table of contents (29 papers)

  1. Front Matter

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  2. Editors’ Introduction

    1. Editors’ Introduction

      • Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
      Pages 1-11
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  4. Regular Contributions

    1. Inductive Inference

      1. Enlarging Learnable Classes
        • Sanjay Jain, Timo Kötzing, Frank Stephan
        Pages 36-50
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      2. Confident and Consistent Partial Learning of Recursive Functions
        • Ziyuan Gao, Frank Stephan
        Pages 51-65
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      3. Automatic Learning from Positive Data and Negative Counterexamples
        • Sanjay Jain, Efim Kinber
        Pages 66-80
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      4. Regular Inference as Vertex Coloring
        • Christophe Costa Florêncio, Sicco Verwer
        Pages 81-95
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    2. Teaching and PAC-Learning

      1. Sauer’s Bound for a Notion of Teaching Complexity
        • Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles
        Pages 96-110
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      2. On the Learnability of Shuffle Ideals
        • Dana Angluin, James Aspnes, Aryeh Kontorovich
        Pages 111-123
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    3. Statistical Learning Theory and Classification

      1. New Analysis and Algorithm for Learning with Drifting Distributions
        • Mehryar Mohri, Andres Muñoz Medina
        Pages 124-138
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      2. On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples
        • Shai Ben-David, Ruth Urner
        Pages 139-153
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      3. Efficient Protocols for Distributed Classification and Optimization
        • Hal Daumé III, Jeff M. Phillips, Avishek Saha, Suresh Venkatasubramanian
        Pages 154-168
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    4. Relations between Models and Data

      1. The Safe Bayesian
        • Peter Grünwald
        Pages 169-183
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      2. Data Stability in Clustering: A Closer Look
        • Lev Reyzin
        Pages 184-198
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    5. Bandit Problems

      1. Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
        • Emilie Kaufmann, Nathaniel Korda, Rémi Munos
        Pages 199-213
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      2. Regret Bounds for Restless Markov Bandits
        • Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos
        Pages 214-228
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Other volumes

  1. Algorithmic Learning Theory

Editors and Affiliations

  • Department of Computer Science, Technion, Haifa, Israel

    Nader H. Bshouty

  • Ecolre Normale Sup’erieure, CNRS, INRIA, Paris, France

    Gilles Stoltz

  • Ecole Normale Supérieure de Cachan, Cachan cedex, France

    Nicolas Vayatis

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

    Thomas Zeugmann

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