Algorithmic Learning Theory

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

Editors:

ISBN: 978-3-642-16107-0 (Print) 978-3-642-16108-7 (Online)

Table of contents (32 chapters)

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  1. Front Matter

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  2. Editors’ Introduction

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      Pages 1-10

      Editors’ Introduction

  3. Invited Papers

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      Pages 11-30

      Towards General Algorithms for Grammatical Inference

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      Pages 31-31

      The Blessing and the Curse of the Multiplicative Updates

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      Pages 32-32

      Discovery of Abstract Concepts by a Robot

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      Pages 33-33

      Contrast Pattern Mining and Its Application for Building Robust Classifiers

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      Pages 34-34

      Optimal Online Prediction in Adversarial Environments

  4. Regular Contributions

    1. Query Learning and Algorithmic Teaching

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        Pages 179-193

        A Lower Bound for Learning Distributions Generated by Probabilistic Automata

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        Pages 194-208

        Lower Bounds on Learning Random Structures with Statistical Queries

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        Pages 209-223

        Recursive Teaching Dimension, Learning Complexity, and Maximum Classes

      On-line Learning

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        Pages 224-238

        Toward a Classification of Finite Partial-Monitoring Games

      Statistical Learning

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        Pages 35-49

        An Algorithm for Iterative Selection of Blocks of Features

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        Pages 50-58

        Bayesian Active Learning Using Arbitrary Binary Valued Queries

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        Pages 59-73

        Approximation Stability and Boosting

      Grammatical Inference and Graph Learning

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        Pages 74-88

        A Spectral Approach for Probabilistic Grammatical Inference on Trees

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        Pages 89-103

        PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation

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        Pages 104-118

        Inferring Social Networks from Outbreaks

      Probably Approximately Correct Learning

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        Pages 119-133

        Distribution-Dependent PAC-Bayes Priors

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        Pages 134-147

        PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar

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        Pages 148-162

        A PAC-Bayes Bound for Tailored Density Estimation

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        Book Chapter

        Pages 163-178

        Compressed Learning with Regular Concept

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