By Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
This e-book 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 foreign convention on Discovery technological know-how, DS 2013. The 23 papers awarded during this quantity have been conscientiously reviewed and chosen from 39 submissions. moreover 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 thought, statistical studying concept, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.
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Extra info for Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings
Ordering by weighted number of wins gives a good ranking for weighted tournaments. ACM Trans. : Spearman’s footrule as a measure of disarray. : Rank aggregation methods for the web. In: Proceedings of the Tenth International Conference on the World Wide Web, WWW 2010, Hong Kong, pp. : Comparing and aggregating rankings with ties. In: Proceedings of the Twenty-Third ACM SIGMODSIGACT-SIGART Symposium on Principles of Database Systems, pp. : A latent pairwise preference learning approach for recommendation from implicit feedback.
S HodgeRank  is an eﬃcient algorithm for ranking data from incomplete pairwise information using combinatorial Hodge theory. The computational problem of sorting data from inconsistent pairwise comparisons has been studied as early as 1990 by Feige et al.  and more recently by Braverman et al. , under various random noise models. NP-Hardness of computing outcomes in various voting systems were established by Bartholdi et al. as early as 1989 . Coppersmith et al.  prove that Borda’s election outcome is a 5-approximation solution to the problem of ﬁnding a Kemeny optimal ranking.
Nagano shows that when Φ is separable, then the Projection can be easily reduced to a submodular function minimization (SFM) problem . For a submodular function f with f (∅) = 0, the submodular function minimization (SFM) for f is the problem of ﬁnding a subset S ⊆ [n] that minimizes f (S). Many combinatorial SFM algorithms are known (see ), and the fastest known strongly polynomial time algorithm of  runs in O(n6 + n5 EO) time, where EO is the unit time to evaluate the value of the submodular function f .
Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann