By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
This ebook constitutes the court cases of the twenty fifth foreign convention on Algorithmic studying idea, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the seventeenth foreign convention on Discovery technological know-how, DS 2014. The 21 papers offered during this quantity have been conscientiously reviewed and chosen from 50 submissions. furthermore the booklet includes four complete papers summarizing the invited talks. The papers are equipped in topical sections named: inductive inference; specified studying from queries; reinforcement studying; on-line studying and studying with bandit info; statistical studying concept; privateness, clustering, MDL, and Kolmogorov complexity.
Read or Download Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings PDF
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Extra resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
An arm is then eliminated as soon as there is another arm with a signiﬁcantly higher score. In the regret analysis of BTM, a high probability bound is provided for a ﬁnite time horizon. More precisely, the regret accumulated by BTM is O γ7K log T minj=i∗ Δi∗ ,j with high probability. This result is stronger than the one proven for IF, in which only the expected regret is upper bounded. Moreover, this high probability regret bound matches with the expected regret bound in the case γ = 1 (strong stochastic transitivity).
258–265 (2013) 38 R. Busa-Fekete and E.
In the latter, feedback is typically represented in a purely qualitative way, namely in terms of pairwise comparisons or rankings. Feedback of this kind can be useful in online learning, too, as has been shown in online information retrieval [28, 42]. As another example, think of crowd-sourcing services like the Amazon Mechanical Turk, where simple questions such as pairwise comparisons between decision alternatives are asked to a group of annotators. The task is to approximate an underlying target ranking on the basis of these pairwise comparisons, which are possibly noisy and partially inconsistent .