Download Algorithmic Learning Theory: 25th International Conference, by Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles PDF

By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles

This publication constitutes the court cases of the twenty fifth foreign convention on Algorithmic studying conception, 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 awarded during this quantity have been conscientiously reviewed and chosen from 50 submissions. additionally the ebook comprises four complete papers summarizing the invited talks. The papers are equipped in topical sections named: inductive inference; certain studying from queries; reinforcement studying; on-line studying and studying with bandit info; statistical studying thought; privateness, clustering, MDL, and Kolmogorov complexity.

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Extra resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings

Example text

The aim of this paper is to provide a survey of the state-of-the-art in the field of preference-based multiarmed bandits (PB-MAB). After recalling the basic setting of the problem in Section 2, we provide an overview of methods that have been proposed to tackle PB-MAB problems in Sections 3 and 4. Our main criterion for systematization is the assumptions made by these methods about the data-generating process or, more specifically, the properties of the pairwise comparisons between arms. Our survey is focused on the stochastic MAB setup, in which feedback is generated according to an underlying (unknown but stationary) probabilistic process; we do not cover the case of an adversarial data-generating processes, although this setting has recently received a lot of attention, too [1, 15, 14].

Then, it determines the stationary distribution (v1 , . . , the eigenvector corresponding to the largest eigenvalue 1). Finally, the options are sorted according to these probabilities: ai RW aj iff vi > vj . The RW ranking is directly motivated by the PageRank algorithm [7], which has been well 34 R. Busa-Fekete and E. H¨ ullermeier studied in social choice theory [3, 6] and rank aggregation [41], and which is widely used in many application fields [7, 32]. Top-k Selection. The learning problem considered in [12] is to find, for some k < K, the top-k arms with respect to the above ranking procedures with high probability.

We are going to distinguish two types of regret bound. The first one is the expected regret bound, which is of the form E RT ≤ B(Q, K, T ) , (3) where E [·] is the expected value operator, RT is the regret accumulated till time step T , and B(·) is a positive real-valued function with the following arguments: the pairwise probabilities Q, the number of arms K, and the iteration number T . This function may additionally depend on parameters of the learner, however, we neglect this dependence here.

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