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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Editors’ Introduction
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    Chapter 2 Cellular Tree Classifiers
  4. Altmetric Badge
    Chapter 3 A Survey of Preference-Based Online Learning with Bandit Algorithms
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    Chapter 4 A Map of Update Constraints in Inductive Inference
  6. Altmetric Badge
    Chapter 5 On the Role of Update Constraints and Text-Types in Iterative Learning
  7. Altmetric Badge
    Chapter 6 Parallel Learning of Automatic Classes of Languages
  8. Altmetric Badge
    Chapter 7 Algorithmic Identification of Probabilities Is Hard
  9. Altmetric Badge
    Chapter 8 Learning Boolean Halfspaces with Small Weights from Membership Queries
  10. Altmetric Badge
    Chapter 9 On Exact Learning Monotone DNF from Membership Queries
  11. Altmetric Badge
    Chapter 10 Learning Regular Omega Languages
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    Chapter 11 Selecting Near-Optimal Approximate State Representations in Reinforcement Learning
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    Chapter 12 Policy Gradients for CVaR-Constrained MDPs
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    Chapter 13 Bayesian Reinforcement Learning with Exploration
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    Chapter 14 Extreme State Aggregation beyond MDPs
  16. Altmetric Badge
    Chapter 15 On Learning the Optimal Waiting Time
  17. Altmetric Badge
    Chapter 16 Bandit Online Optimization over the Permutahedron
  18. Altmetric Badge
    Chapter 17 Offline to Online Conversion
  19. Altmetric Badge
    Chapter 18 A Chain Rule for the Expected Suprema of Gaussian Processes
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    Chapter 19 Generalization Bounds for Time Series Prediction with Non-stationary Processes
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    Chapter 20 Generalizing Labeled and Unlabeled Sample Compression to Multi-label Concept Classes
  22. Altmetric Badge
    Chapter 21 Robust and Private Bayesian Inference
  23. Altmetric Badge
    Chapter 22 Clustering, Hamming Embedding, Generalized LSH and the Max Norm
  24. Altmetric Badge
    Chapter 23 Indefinitely Oscillating Martingales
  25. Altmetric Badge
    Chapter 24 A Safe Approximation for Kolmogorov Complexity
Attention for Chapter 18: A Chain Rule for the Expected Suprema of Gaussian Processes
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Chapter title
A Chain Rule for the Expected Suprema of Gaussian Processes
Chapter number 18
Book title
Algorithmic Learning Theory
Published in
arXiv, October 2014
DOI 10.1007/978-3-319-11662-4_18
Book ISBNs
978-3-31-911661-7, 978-3-31-911662-4
Authors

Andreas Maurer

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 Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 November 2014.
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#20,242,779
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#669,271
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#213,114
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