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 Models for Autonomously Motivated Exploration in Reinforcement Learning
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   Chapter 3 On the Expressive Power of Deep Architectures
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   Chapter 4 Optimal Estimation
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   Chapter 5 Learning from Label Preferences
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   Chapter 6 Information Distance and Its Extensions
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   Chapter 7 Iterative Learning from Positive Data and Counters
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   Chapter 8 Algorithmic Learning Theory
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    Chapter 9 Algorithmic Learning Theory
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    Chapter 10 Learning Relational Patterns
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    Chapter 11 Adaptive and Optimal Online Linear Regression on ℓ 1 -Balls
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    Chapter 12 Re-adapting the Regularization of Weights for Non-stationary Regression
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    Chapter 13 Competing against the Best Nearest Neighbor Filter in Regression
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    Chapter 14 Lipschitz Bandits without the Lipschitz Constant
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    Chapter 15 Deviations of Stochastic Bandit Regret
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    Chapter 16 On Upper-Confidence Bound Policies for Switching Bandit Problems
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    Chapter 17 Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits
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    Chapter 18 The Perceptron with Dynamic Margin
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    Chapter 19 Combining Initial Segments of Lists
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    Chapter 20 Algorithmic Learning Theory
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    Chapter 21 Making Online Decisions with Bounded Memory
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    Chapter 22 Universal Prediction of Selected Bits
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    Chapter 23 Semantic Communication for Simple Goals Is Equivalent to On-line Learning
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    Chapter 24 Accelerated Training of Max-Margin Markov Networks with Kernels
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    Chapter 25 Domain Adaptation in Regression
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    Chapter 26 Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization
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    Chapter 27 Axioms for Rational Reinforcement Learning
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    Chapter 28 Universal Knowledge-Seeking Agents
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    Chapter 29 Asymptotically Optimal Agents
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    Chapter 30 Time Consistent Discounting
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    Chapter 31 Distributional Learning of Simple Context-Free Tree Grammars
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    Chapter 32 On Noise-Tolerant Learning of Sparse Parities and Related Problems
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    Chapter 33 Supervised Learning and Co-training
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    Chapter 34 Learning a Classifier when the Labeling Is Known
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    Chapter 35 Erratum: Learning without Coding