21. ALT 2010: Canberra, Australia

• 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 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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