Algorithmic Learning Theory 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings /
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...
| Corporate Author: | |
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| Other Authors: | , , , |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Edition: | 1st ed. 2013. |
| Series: | Lecture Notes in Artificial Intelligence ;
8139 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-642-40935-6 |
Table of 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.