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Algorithmic Learning Theory

21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings

  • Conference proceedings
  • © 2010

Overview

Editors:
  1. Marcus Hutter

    1. Research School of Information Sciences and Engineering, Australian National University and NICTA, Canberra, Australia

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  2. Frank Stephan

    1. Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore

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  3. Vladimir Vovk

    1. Department of Computer Science, University of London, Royal Holloway, Egham, Surrey, UK

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  4. Thomas Zeugmann

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6331)

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

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Conference proceedings info: ALT 2010.

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Table of contents (32 papers)

  1. Front Matter

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  2. Editors’ Introduction

    1. Editors’ Introduction

      • Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann
      Pages 1-10

  4. Regular Contributions

    1. Statistical Learning

      1. An Algorithm for Iterative Selection of Blocks of Features
        • Pierre Alquier
        Pages 35-49
      2. Bayesian Active Learning Using Arbitrary Binary Valued Queries
        • Liu Yang, Steve Hanneke, Jaime Carbonell
        Pages 50-58
      3. Approximation Stability and Boosting
        • Wei Gao, Zhi-Hua Zhou
        Pages 59-73

    2. Grammatical Inference and Graph Learning

      1. A Spectral Approach for Probabilistic Grammatical Inference on Trees
        • Raphaël Bailly, Amaury Habrard, François Denis
        Pages 74-88
      2. PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation
        • Balázs Csanád Csáji, Raphaël M. Jungers, Vincent D. Blondel
        Pages 89-103
      3. Inferring Social Networks from Outbreaks
        • Dana Angluin, James Aspnes, Lev Reyzin
        Pages 104-118

    3. Probably Approximately Correct Learning

      1. Distribution-Dependent PAC-Bayes Priors
        • Guy Lever, François Laviolette, John Shawe-Taylor
        Pages 119-133
      2. PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar
        • Vladimir Pestov
        Pages 134-147
      3. A PAC-Bayes Bound for Tailored Density Estimation
        • Matthew Higgs, John Shawe-Taylor
        Pages 148-162
      4. Compressed Learning with Regular Concept
        • Jiawei Lv, Jianwen Zhang, Fei Wang, Zheng Wang, Changshui Zhang
        Pages 163-178

    4. Query Learning and Algorithmic Teaching

      1. A Lower Bound for Learning Distributions Generated by Probabilistic Automata
        • Borja Balle, Jorge Castro, Ricard Gavaldà
        Pages 179-193
      2. Lower Bounds on Learning Random Structures with Statistical Queries
        • Dana Angluin, David Eisenstat, Leonid (Aryeh) Kontorovich, Lev Reyzin
        Pages 194-208
      3. Recursive Teaching Dimension, Learning Complexity, and Maximum Classes
        • Thorsten Doliwa, Hans Ulrich Simon, Sandra Zilles
        Pages 209-223
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  1. Algorithmic Learning Theory

Keywords

About this book

This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it wasco-located and held in parallel with Algorithmic Learning Theory.

Editors and Affiliations

  • Research School of Information Sciences and Engineering, Australian National University and NICTA, Canberra, Australia

    Marcus Hutter

  • Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore

    Frank Stephan

  • Department of Computer Science, University of London, Royal Holloway, Egham, Surrey, UK

    Vladimir Vovk

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

    Thomas Zeugmann

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