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Algorithmic Learning Theory

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Cover of '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 Learning and Optimizing with Preferences
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    Chapter 3 Efficient Algorithms for Combinatorial Online Prediction
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    Chapter 4 Exact Learning from Membership Queries: Some Techniques, Results and New Directions
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    Chapter 5 Universal Algorithm for Trading in Stock Market Based on the Method of Calibration
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    Chapter 6 Combinatorial Online Prediction via Metarounding
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    Chapter 7 On Competitive Recommendations
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    Chapter 8 Online PCA with Optimal Regrets
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    Chapter 9 Partial Learning of Recursively Enumerable Languages
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    Chapter 10 Topological Separations in Inductive Inference
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    Chapter 11 PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data
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    Chapter 12 Universal Knowledge-Seeking Agents for Stochastic Environments
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    Chapter 13 Order Compression Schemes
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    Chapter 14 Learning a Bounded-Degree Tree Using Separator Queries
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    Chapter 15 Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates
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    Chapter 16 Robust Risk-Averse Stochastic Multi-armed Bandits
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    Chapter 17 An Efficient Algorithm for Learning with Semi-bandit Feedback
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    Chapter 18 Differentially-Private Learning of Low Dimensional Manifolds
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    Chapter 19 Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data
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    Chapter 20 Adaptive Metric Dimensionality Reduction
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    Chapter 21 Dimension-Adaptive Bounds on Compressive FLD Classification
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    Chapter 22 Bayesian methods for low-rank matrix estimation: short survey and theoretical study
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    Chapter 23 Concentration and Confidence for Discrete Bayesian Sequence Predictors
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    Chapter 24 Algorithmic Connections between Active Learning and Stochastic Convex Optimization
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    Chapter 25 Unsupervised Model-Free Representation Learning
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    Chapter 26 Fast Spectral Clustering via the Nyström Method
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    Chapter 27 Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series
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Title
Algorithmic Learning Theory
Published by
Springer Berlin Heidelberg, January 2013
DOI 10.1007/978-3-642-40935-6
ISBNs
978-3-64-240934-9, 978-3-64-240935-6
Editors

Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Researcher 1 33%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Economics, Econometrics and Finance 1 33%
Engineering 1 33%
Design 1 33%