By Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
This booklet constitutes the court cases of the twenty fourth foreign convention on Algorithmic studying conception, ALT 2013, held in Singapore in October 2013, and co-located with the sixteenth overseas convention on Discovery technological know-how, DS 2013. The 23 papers offered during this quantity have been rigorously reviewed and chosen from 39 submissions. furthermore the ebook comprises three complete papers of invited talks. The papers are equipped in topical sections named: on-line studying, inductive inference and grammatical inference, educating and studying from queries, bandit idea, statistical studying thought, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.
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Extra resources for Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings
Hedging Structured Concepts. In: Proceedings of the 23rd Conference on Learning Theory, COLT 2010, pp. : A faster parametric submodular function minimization algorithm and applications. : A Faster Strongly Polynomial Time Algorithm for Submodular Function Minimization. P. ) IPCO 2007. LNCS, vol. 4513, pp. 240–251. : Theory of linear and integer programming. : Improved approximations of packing and covering problems. In: 27th ACM Symposium on the Theory of Computing, pp. : Online Prediction under Submodular Constraints.
Artif. Intell. : Statistical ranking and combinatorial hodge theory. Math. Program. : Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. : Mathematics without numbers. : How to rank with few errors. In: STOC, pp. : Analyzing and Modeling Rank Data. : Ranking from pairs and triplets: information quality, evaluation methods and query complexity. In: WSDM, pp. : Learning diverse rankings with multiarmed bandits.
Examples of such classes C are permutations (with Kendall tau loss) , set covers, and truth assignments to a CNF formula, for which the corresponding oﬄine linear optimization problems are the minimum feedback arc set problem, the minimum set cover problem, and the MAXSAT problem, respectively. So we change our goal. Assume that we have an α-approximation algorithm for OPT(C). Then our second goal is to minimize the following α-regret: T T ct · E[ t=1 c∗ · t ] − α min ∗ c ∈C t. t=1 With a generalization of the FTRL framework, Kakade et al.
Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann