Algorithmic Learning Theory
24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings
Jain, Sanjay.
editor.
Munos, Rémi.
editor.
Stephan, Frank.
editor.
Zeugmann, Thomas.
editor.
SpringerLink (Online service)
text
gw
2013
monographic
eng
access
XVIII, 397 p. 30 illus. online resource.
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. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning.
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.
edited by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann.
Computer science
Computers
Algorithms
Computer logic
Mathematical logic
Artificial intelligence
Pattern recognition
Computer Science
Artificial Intelligence (incl. Robotics)
Mathematical Logic and Formal Languages
Algorithm Analysis and Problem Complexity
Computation by Abstract Devices
Logics and Meanings of Programs
Pattern Recognition
Q334-342
TJ210.2-211.495
006.3
Springer eBooks
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