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Algorithmic Learning Theory

19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings

  • Yoav Freund
  • László Györfi
  • György Turán
  • Thomas Zeugmann
Conference proceedings ALT 2008

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5254)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 5254)

Table of contents

  1. Front Matter
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  2. Invited Papers

    1. On Iterative Algorithms with an Information Geometry Background
      Imre Csiszár
      Pages 1-1
    2. Visual Analytics: Combining Automated Discovery with Interactive Visualizations
      Daniel A. Keim, Florian Mansmann, Daniela Oelke, Hartmut Ziegler
      Pages 2-2
    3. Some Mathematics behind Graph Property Testing
      László Lovász
      Pages 3-3
    4. Finding Total and Partial Orders from Data for Seriation
      Heikki Mannila
      Pages 4-4
    5. Computational Models of Neural Representations in the Human Brain
      Tom M. Mitchell
      Pages 5-6

  3. Regular Contributions

    1. Statistical Learning

      1. Generalization Bounds for Some Ordinal Regression Algorithms
        Shivani Agarwal
        Pages 7-21
      2. Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm
        Stéphan Clémençon, Nicolas Vayatis
        Pages 22-37
      3. Sample Selection Bias Correction Theory
        Corinna Cortes, Mehryar Mohri, Michael Riley, Afshin Rostamizadeh
        Pages 38-53
      4. Exploiting Cluster-Structure to Predict the Labeling of a Graph
        Mark Herbster
        Pages 54-69
      5. A Uniform Lower Error Bound for Half-Space Learning
        Andreas Maurer, Massimiliano Pontil
        Pages 70-78
      6. Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
        Andreas Maurer, Massimiliano Pontil
        Pages 79-91
      7. Learning and Generalization with the Information Bottleneck
        Ohad Shamir, Sivan Sabato, Naftali Tishby
        Pages 92-107

    2. Probability and Stochastic Processes

      1. Growth Optimal Investment with Transaction Costs
        László Györfi, István Vajda
        Pages 108-122
      2. Online Regret Bounds for Markov Decision Processes with Deterministic Transitions
        Ronald Ortner
        Pages 123-137
      3. On-Line Probability, Complexity and Randomness
        Alexey Chernov, Alexander Shen, Nikolai Vereshchagin, Vladimir Vovk
        Pages 138-153
      4. Prequential Randomness
        Vladimir Vovk, Alexander Shen
        Pages 154-168
      5. Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor
        Daniil Ryabko
        Pages 169-182
      6. Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
        Arthur Gretton, László Györfi
        Pages 183-198

    3. Boosting and Experts

      1. Supermartingales in Prediction with Expert Advice
        Alexey Chernov, Yuri Kalnishkan, Fedor Zhdanov, Vladimir Vovk
        Pages 199-213
      2. Aggregating Algorithm for a Space of Analytic Functions
        Mikhail Dashevskiy
        Pages 214-226
      3. Smooth Boosting for Margin-Based Ranking
        Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda
        Pages 227-239
      4. Learning with Continuous Experts Using Drifting Games
        Indraneel Mukherjee, Robert E. Schapire
        Pages 240-255
      5. Entropy Regularized LPBoost
        Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan
        Pages 256-271

    4. Active Learning and Queries

      1. Optimally Learning Social Networks with Activations and Suppressions
        Dana Angluin, James Aspnes, Lev Reyzin
        Pages 272-286
      2. Active Learning in Multi-armed Bandits
        András Antos, Varun Grover, Csaba Szepesvári
        Pages 287-302
      3. Query Learning and Certificates in Lattices
        M. Arias, J. L. Balcázar
        Pages 303-315
      4. Clustering with Interactive Feedback
        Maria-Florina Balcan, Avrim Blum
        Pages 316-328
      5. Active Learning of Group-Structured Environments
        Gábor Bartók, Csaba Szepesvári, Sandra Zilles
        Pages 329-343
      6. Finding the Rare Cube
        Shlomo Hoory, Oded Margalit
        Pages 344-358

    5. Inductive Inference

      1. Iterative Learning of Simple External Contextual Languages
        Leonor Becerra-Bonache, John Case, Sanjay Jain, Frank Stephan
        Pages 359-373
      2. Topological Properties of Concept Spaces
        Matthew de Brecht, Akihiro Yamamoto
        Pages 374-388
      3. Dynamically Delayed Postdictive Completeness and Consistency in Learning
        John Case, Timo Kötzing
        Pages 389-403
      4. Dynamic Modeling in Inductive Inference
        John Case, Timo Kötzing
        Pages 404-418
      5. Optimal Language Learning
        John Case, Samuel E. Moelius III
        Pages 419-433
      6. Numberings Optimal for Learning
        Sanjay Jain, Frank Stephan
        Pages 434-448
      7. Learning with Temporary Memory
        Steffen Lange, Samuel E. Moelius III, Sandra Zilles
        Pages 449-463

    6. Erratum

      1. Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors
        Jittat Fakcharoenphol, Boonserm Kijsirikul
        Pages 464-466
  4. Back Matter
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About these proceedings

Introduction

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008.

The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.

Keywords

Clustering comlexity concept spaces half-space learning language learning learning machine learning markov process neural representation visual datamining visualization

Editors and affiliations

  • Yoav Freund
    • 1
  • László Györfi
    • 2
  • György Turán
    • 3
  • Thomas Zeugmann
    • 4
  1. 1.Computer Science and EngineeringUniversity of CaliforniaSan DiegoUSA
  2. 2.Department of Computer Science and Information TheoryDepartment of Computer Science and Budapest University of Technology and EconomicsBudapestHungary
  3. 3.Department of Math., Stat. and Comp. Sci,University of IllinoisChicagoUSA
  4. 4.Division of Computer ScienceHokkaido UniversitySapporoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-87987-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-87986-2
  • Online ISBN 978-3-540-87987-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site