Learning Theory

Table of Contents

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   Book Overview
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   Chapter 1 Learning Theory
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   Chapter 2 Graphical Economics
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   Chapter 3 Deterministic Calibration and Nash Equilibrium
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   Chapter 4 Reinforcement Learning for Average Reward Zero-Sum Games
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   Chapter 5 Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability
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   Chapter 6 Minimizing Regret with Label Efficient Prediction
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   Chapter 7 Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
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   Chapter 8 Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
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    Chapter 9 Learning Classes of Probabilistic Automata
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    Chapter 10 On the Learnability of E-pattern Languages over Small Alphabets
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    Chapter 11 Replacing Limit Learners with Equally Powerful One-Shot Query Learners
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    Chapter 12 Concentration Bounds for Unigrams Language Model
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    Chapter 13 Inferring Mixtures of Markov Chains
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    Chapter 14 PExact = Exact Learning
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    Chapter 15 Learning a Hidden Graph Using O(log n) Queries Per Edge
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    Chapter 16 Toward Attribute Efficient Learning of Decision Lists and Parities
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    Chapter 17 Learning Over Compact Metric Spaces
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    Chapter 18 A Function Representation for Learning in Banach Spaces
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    Chapter 19 Local Complexities for Empirical Risk Minimization
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    Chapter 20 Model Selection by Bootstrap Penalization for Classification
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    Chapter 21 Convergence of Discrete MDL for Sequential Prediction
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    Chapter 22 On the Convergence of MDL Density Estimation
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    Chapter 23 Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification
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    Chapter 24 Learning Intersections of Halfspaces with a Margin
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    Chapter 25 A General Convergence Theorem for the Decomposition Method
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    Chapter 26 Oracle Bounds and Exact Algorithm for Dyadic Classification Trees
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    Chapter 27 An Improved VC Dimension Bound for Sparse Polynomials
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    Chapter 28 A New PAC Bound for Intersection-Closed Concept Classes
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    Chapter 29 A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering
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    Chapter 30 Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers
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    Chapter 31 Consistency in Models for Communication Constrained Distributed Learning
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    Chapter 32 On the Convergence of Spectral Clustering on Random Samples: The Normalized Case
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    Chapter 33 Performance Guarantees for Regularized Maximum Entropy Density Estimation
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    Chapter 34 Learning Monotonic Linear Functions
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    Chapter 35 Learning Theory
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    Chapter 36 Bayesian Networks and Inner Product Spaces
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    Chapter 37 An Inequality for Nearly Log-Concave Distributions with Applications to Learning
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    Chapter 38 Bayes and Tukey Meet at the Center Point
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    Chapter 39 Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
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    Chapter 40 Learning Theory
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    Chapter 41 Statistical Properties of Kernel Principal Component Analysis
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    Chapter 42 Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA
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    Chapter 43 Regularization and Semi-supervised Learning on Large Graphs
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    Chapter 44 Perceptron-Like Performance for Intersections of Halfspaces
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    Chapter 45 The Optimal PAC Algorithm
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    Chapter 46 The Budgeted Multi-armed Bandit Problem