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 e-book constitutes the lawsuits of the twenty fifth foreign convention on Algorithmic studying concept, 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 provided during this quantity have been rigorously reviewed and chosen from 50 submissions. furthermore the ebook comprises four complete papers summarizing the invited talks. The papers are geared up in topical sections named: inductive inference; distinct studying from queries; reinforcement studying; on-line studying and studying with bandit details; statistical studying concept; privateness, clustering, MDL, and Kolmogorov complexity.

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

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To make this problem feasible, more practical ranking procedures are considered. In fact, the problem of ranking procedures like Copeland is that a minimal change of a value qi,j ≈ 12 may strongly influence the induced order relation CO . Consequently, the number of samples needed to assure (with high probability) a certain approximation quality may become arbitrarily large. A similar problem arises for SE as a target order if some of the individual scores qi are very close or equal to each other.

Then, for any pair of items i and j such that ri < rj , the pairwise probability is given by qi,j = g(ri , rj , φ), where g(i, j, φ) = h(j − i + 1, φ) − h(j − i, φ) 4 Recall that this property is an axiomatic assumption underlying the IF and BTM algorithms. Interestingly, the stochastic triangle inequality, which is also assumed by Yue et al. [44], is not satisfied for Mallows φ-model [36]. 32 R. Busa-Fekete and E. H¨ ullermeier with h(k, φ) = k/(1 − φk ). Based on this result, one can show that the “margin” min |1/2 − qi,j | i=j φ 1 , 1+φ ).

1 Introduction Multi-armed bandit (MAB) algorithms have received considerable attention and have been studied quite intensely in machine learning in the recent past. The great interest in this topic is hardly surprising, given that the MAB setting is not only theoretically challenging but also practically useful, as can be seen from their use in a wide range of applications. For example, MAB algorithms turned out to offer effective solutions for problems in medical treatment design [35, 34], online advertisement [16], and recommendation systems [33], just to mention a few.

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