Algorithmic Learning Theory

Table of Contents

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   Book Overview
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   Chapter 1 Editors’ Introduction
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   Chapter 2 Cellular Tree Classifiers
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   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
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   Chapter 5 On the Role of Update Constraints and Text-Types in Iterative Learning
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   Chapter 6 Parallel Learning of Automatic Classes of Languages
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   Chapter 7 Algorithmic Identification of Probabilities Is Hard
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   Chapter 8 Learning Boolean Halfspaces with Small Weights from Membership Queries
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    Chapter 9 On Exact Learning Monotone DNF from Membership Queries
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    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
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    Chapter 15 On Learning the Optimal Waiting Time
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    Chapter 16 Bandit Online Optimization over the Permutahedron
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    Chapter 17 Offline to Online Conversion
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    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
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    Chapter 21 Robust and Private Bayesian Inference
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    Chapter 22 Clustering, Hamming Embedding, Generalized LSH and the Max Norm
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    Chapter 23 Indefinitely Oscillating Martingales
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    Chapter 24 A Safe Approximation for Kolmogorov Complexity