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Learning Theory

20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Nader H. Bshouty
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  2. Claudio Gentile
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4539)

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

Included in the following conference series:

Conference proceedings info: COLT 2007.

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Table of contents (48 papers)

  1. Front Matter

    Download chapter PDF

  2. Invited Presentations

    1. Property Testing: A Learning Theory Perspective

      • Dana Ron
      Pages 1-2

    2. Spectral Algorithms for Learning and Clustering

      • Santosh S. Vempala
      Pages 3-4

  3. Unsupervised, Semisupervised and Active Learning I

    1. Minimax Bounds for Active Learning

      • Rui M. Castro, Robert D. Nowak
      Pages 5-19

    2. Stability of k-Means Clustering

      • Shai Ben-David, Dávid Pál, Hans Ulrich Simon
      Pages 20-34

    3. Margin Based Active Learning

      • Maria-Florina Balcan, Andrei Broder, Tong Zhang
      Pages 35-50

  4. Unsupervised, Semisupervised and Active Learning II

    1. Learning Large-Alphabet and Analog Circuits with Value Injection Queries

      • Dana Angluin, James Aspnes, Jiang Chen, Lev Reyzin
      Pages 51-65

    2. Teaching Dimension and the Complexity of Active Learning

      • Steve Hanneke
      Pages 66-81

    3. Multi-view Regression Via Canonical Correlation Analysis

      • Sham M. Kakade, Dean P. Foster
      Pages 82-96

  5. Statistical Learning Theory

    1. Aggregation by Exponential Weighting and Sharp Oracle Inequalities

      • Arnak S. Dalalyan, Alexandre B. Tsybakov
      Pages 97-111

    2. Occam’s Hammer

      • Gilles Blanchard, François Fleuret
      Pages 112-126

    3. Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector

      • Sylvain Arlot, Gilles Blanchard, Étienne Roquain
      Pages 127-141

    4. Suboptimality of Penalized Empirical Risk Minimization in Classification

      • Guillaume Lecué
      Pages 142-156

    5. Transductive Rademacher Complexity and Its Applications

      • Ran El-Yaniv, Dmitry Pechyony
      Pages 157-171

  6. Inductive Inference

    1. U-Shaped, Iterative, and Iterative-with-Counter Learning

      • John Case, Samuel E. Moelius III
      Pages 172-186

    2. Mind Change Optimal Learning of Bayes Net Structure

      • Oliver Schulte, Wei Luo, Russell Greiner
      Pages 187-202

    3. Learning Correction Grammars

      • Lorenzo Carlucci, John Case, Sanjay Jain
      Pages 203-217

    4. Mitotic Classes

      • Sanjay Jain, Frank Stephan
      Pages 218-232

  7. Online and Reinforcement Learning I

    1. Regret to the Best vs. Regret to the Average

      • Eyal Even-Dar, Michael Kearns, Yishay Mansour, Jennifer Wortman
      Pages 233-247

    2. Strategies for Prediction Under Imperfect Monitoring

      • Gábor Lugosi, Shie Mannor, Gilles Stoltz
      Pages 248-262
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