Algorithmic Learning Theory

24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings

Häftad, Engelska, 2013

AvSanjay Jain,Rémi Munos,Frank Stephan,Thomas Zeugmann,Remi Munos

709 kr

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This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013.

Produktinformation

Utgivningsdatum2013-09-11
Mått155 x 235 x 23 mm
Vikt628 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor397
Upplaga2013
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642409349

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Editors’ Introduction.- Learning and Optimizing with Preferences.- Efficient Algorithms for Combinatorial Online Prediction.- Exact Learning from Membership Queries: Some Techniques, Results and New Directions.- Online Learning Universal Algorithm for Trading in Stock Market Based on the Method of Calibration.- Combinatorial Online Prediction via Metarounding.- On Competitive Recommendations.- Online PCA with Optimal Regrets.- Inductive Inference and Grammatical Inference Partial Learning of Recursively Enumerable Languages.- Topological Separations in Inductive Inference.- PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data.- Universal Knowledge-Seeking Agents for Stochastic Environments.- Teaching and Learning from Queries Order Compression Schemes.- Learning a Bounded-Degree Tree Using Separator Queries.- Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates.- Robust Risk-Averse Stochastic Multi-armed Bandits.- An Efficient Algorithm for Learning with Semi-bandit Feedback.- Differentially-Private Learning of Low Dimensional Manifolds.- Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data.- Adaptive Metric Dimensionality Reduction.- Dimension-Adaptive Bounds on Compressive FLD Classification.- Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study.- Concentration and Confidence for Discrete Bayesian Sequence Predictors.- Algorithmic Connections between Active Learning and Stochastic Convex Optimization.- Unsupervised/Semi-Supervised Learning Unsupervised Model-Free Representation Learning.- Fast Spectral Clustering via the Nyström Method.- Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series.