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ORCID21. ALT 2010: Canberra, Australia
- Marcus Hutter
, Frank Stephan, Vladimir Vovk, Thomas Zeugmann:
Algorithmic Learning Theory, 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings. Lecture Notes in Computer Science 6331, Springer 2010, ISBN 978-3-642-16107-0 - Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann:
Editors' Introduction. 1-10
Invited Papers
- Alexander Clark:
Towards General Algorithms for Grammatical Inference. 11-30 - Manfred K. Warmuth:
The Blessing and the Curse of the Multiplicative Updates. 31 - Ivan Bratko:
Discovery of Abstract Concepts by a Robot. 32 - Kotagiri Ramamohanarao:
Contrast Pattern Mining and Its Application for Building Robust Classifiers. 33 - Peter L. Bartlett:
Optimal Online Prediction in Adversarial Environments. 34
Statistical Learning
- Pierre Alquier:
An Algorithm for Iterative Selection of Blocks of Features. 35-49 - Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bayesian Active Learning Using Arbitrary Binary Valued Queries. 50-58 - Wei Gao, Zhi-Hua Zhou:
Approximation Stability and Boosting. 59-73
Grammatical Inference and Graph Learning
- Raphaël Bailly, Amaury Habrard, François Denis:
A Spectral Approach for Probabilistic Grammatical Inference on Trees. 74-88 - Balázs Csanád Csáji, Raphaël M. Jungers, Vincent D. Blondel:
PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation. 89-103 - Dana Angluin, James Aspnes, Lev Reyzin:
Inferring Social Networks from Outbreaks. 104-118
Probably Approximately Correct Learning
- Guy Lever, François Laviolette, John Shawe-Taylor:
Distribution-Dependent PAC-Bayes Priors. 119-133 - Vladimir Pestov:
PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar. 134-147 - Matthew Higgs, John Shawe-Taylor:
A PAC-Bayes Bound for Tailored Density Estimation. 148-162 - Jiawei Lv, Jianwen Zhang, Fei Wang, Zheng Wang, Changshui Zhang:
Compressed Learning with Regular Concept. 163-178
Query Learning and Algorithmic Teaching
- Borja Balle, Jorge Castro, Ricard Gavaldà:
A Lower Bound for Learning Distributions Generated by Probabilistic Automata. 179-193 - Dana Angluin, David Eisenstat, Leonid Kontorovich, Lev Reyzin:
Lower Bounds on Learning Random Structures with Statistical Queries. 194-208 - Thorsten Doliwa, Hans Ulrich Simon, Sandra Zilles:
Recursive Teaching Dimension, Learning Complexity, and Maximum Classes. 209-223
On-line Learning
- Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Toward a Classification of Finite Partial-Monitoring Games. 224-238 - Wouter M. Koolen, Steven de Rooij:
Switching Investments. 239-254 - Alexey V. Chernov, Fedor Zhdanov:
Prediction with Expert Advice under Discounted Loss. 255-269 - Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton:
A Regularization Approach to Metrical Task Systems. 270-284
Inductive Inference
- John Case, Timo Kötzing:
Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations. 285-299 - Samuel E. Moelius, Sandra Zilles:
Learning without Coding. 300-314 - Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto:
Learning Figures with the Hausdorff Metric by Fractals. 315-329 - Sanjay Jain, Efim B. Kinber:
Inductive Inference of Languages from Samplings. 330-344
Reinforcement Learning
- Laurent Orseau:
Optimality Issues of Universal Greedy Agents with Static Priors. 345-359 - Peter Sunehag, Marcus Hutter:
Consistency of Feature Markov Processes. 360-374 - Taishi Uchiya, Atsuyoshi Nakamura, Mineichi Kudo:
Algorithms for Adversarial Bandit Problems with Multiple Plays. 375-389
On-line Learning and Kernel Methods
- Rong Jin, Steven C. H. Hoi, Tianbao Yang:
Online Multiple Kernel Learning: Algorithms and Mistake Bounds. 390-404 - Fedor Zhdanov, Yuri Kalnishkan:
An Identity for Kernel Ridge Regression. 405-419
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