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Algorithmic Learning Theory

22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings

  • Conference proceedings
  • © 2011

Overview

Editors:
  1. Jyrki Kivinen

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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  2. Csaba Szepesvári

    1. Department of Computing Science, University of Alberta, Edmonton, Canada

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  3. Esko Ukkonen

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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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 6925)

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

Included in the following conference series:

Conference proceedings info: ALT 2011.

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Table of contents (35 papers)

  1. Front Matter

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  2. Editors’ Introduction

    1. Editors’ Introduction

      • Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann
      Pages 1-13

  3. Invited Papers

    1. Models for Autonomously Motivated Exploration in Reinforcement Learning

      • Peter Auer, Shiau Hong Lim, Chris Watkins
      Pages 14-17

    2. On the Expressive Power of Deep Architectures

      • Yoshua Bengio, Olivier Delalleau
      Pages 18-36

    3. Optimal Estimation

      • Jorma Rissanen
      Pages 37-37

    4. Learning from Label Preferences

      • Eyke Hüllermeier, Johannes Fürnkranz
      Pages 38-38

    5. Information Distance and Its Extensions

      • Ming Li
      Pages 39-39

  4. Inductive Inference

    1. Iterative Learning from Positive Data and Counters

      • Timo Kötzing
      Pages 40-54

    2. Robust Learning of Automatic Classes of Languages

      • Sanjay Jain, Eric Martin, Frank Stephan
      Pages 55-69

    3. Learning and Classifying

      • Sanjay Jain, Eric Martin, Frank Stephan
      Pages 70-83

    4. Learning Relational Patterns

      • Michael Geilke, Sandra Zilles
      Pages 84-98

  5. Regression

    1. Adaptive and Optimal Online Linear Regression on ℓ1-Balls

      • Sébastien Gerchinovitz, Jia Yuan Yu
      Pages 99-113

    2. Re-adapting the Regularization of Weights for Non-stationary Regression

      • Nina Vaits, Koby Crammer
      Pages 114-128

    3. Competing against the Best Nearest Neighbor Filter in Regression

      • Arnak S. Dalalyan, Joseph Salmon
      Pages 129-143

  6. Bandit Problems

    1. Lipschitz Bandits without the Lipschitz Constant

      • Sébastien Bubeck, Gilles Stoltz, Jia Yuan Yu
      Pages 144-158

    2. Deviations of Stochastic Bandit Regret

      • Antoine Salomon, Jean-Yves Audibert
      Pages 159-173

    3. On Upper-Confidence Bound Policies for Switching Bandit Problems

      • Aurélien Garivier, Eric Moulines
      Pages 174-188

    4. Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits

      • Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer
      Pages 189-203

  7. Online Learning

    1. The Perceptron with Dynamic Margin

      • Constantinos Panagiotakopoulos, Petroula Tsampouka
      Pages 204-218

    2. Combining Initial Segments of Lists

      • Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
      Pages 219-233
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About this book

This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011.
The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models.

Editors and Affiliations

  • Department of Computer Science, University of Helsinki, Helsinki, Finland

    Jyrki Kivinen, Esko Ukkonen

  • Department of Computing Science, University of Alberta, Edmonton, Canada

    Csaba Szepesvári

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

    Thomas Zeugmann

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