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Algorithmic Learning Theory [electronic resource] : 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings / edited by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann.

Contributor(s): Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science ; 8139Publication details: Berlin, Heidelberg : Springer Berlin Heidelberg, 2013.Description: 1 online resource (XVIII, 397 p. 30 ill.)ISBN:
  • 9783642409356
Subject(s): Online resources:
Contents:
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.
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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.

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