Download e-book for iPad: Algorithmic Learning Theory: 20th International Conference, by Ricard Gavaldà, Gabor Lugosi, Thomas Zeugmann, Sandra Zilles

By Ricard Gavaldà, Gabor Lugosi, Thomas Zeugmann, Sandra Zilles

ISBN-10: 3642044131

ISBN-13: 9783642044137

This e-book constitutes the refereed lawsuits of the twentieth overseas convention on Algorithmic studying conception, ALT 2009, held in Porto, Portugal, in October 2009, co-located with the twelfth foreign convention on Discovery technological know-how, DS 2009. The 26 revised complete papers offered including the abstracts of five invited talks have been conscientiously reviewed and chosen from 60 submissions. The papers are divided into topical sections of papers on on-line studying, studying graphs, energetic studying and question studying, statistical studying, inductive inference, and semisupervised and unsupervised studying. the amount additionally includes abstracts of the invited talks: Sanjoy Dasgupta, the 2 Faces of energetic studying; Hector Geffner, Inference and studying in making plans; Jiawei Han, Mining Heterogeneous; details Networks through Exploring the ability of hyperlinks, Yishay Mansour, studying and area edition; Fernando C.N. Pereira, studying on the net.

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Extra info for Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009, Proceedings

Example text

Lower bounds are also indicated. The symbols denote the universal constants, whereas the are distribution-dependent constants. Distribution-dependent EDP e− Uniform UCB(p) Lower bound EBA (p ln n)/n MPA EDP n n− e− Distribution-free n2(1−p) n pK ln n n EBA K ln K n √ p ln n MPA pK ln n n K n Table 1 indicates that while for distribution-dependent bounds, the asymptotic optimal rate of decrease in the number n of rounds √ for simple regrets is exponential, for distribution-free bounds, the rate worsens to 1/ n.

We state this result for the slight modification UCB(p) of UCB1 stated in Figure 2; its proof relies on noting that it achieves a cumulative regret bounded by a large enough distribution-dependent constant times ε(n) = p ln n. Corollary 2. The allocation strategy (ϕt ) given by the forecaster UCB(p) of Figure 2 ensures that for all recommendation strategies (ψt ) and all sets of K 3 (distinct, Bernoulli) distributions on the rewards, there exist two constants β > 0 and γ 0 (independent of p) such that, up to the choice of a good ordering of the considered distributions, Ern β n−γp .

32 S. Bubeck, R. Munos, and G. Stoltz Parameters: the history I1 , . . , In of played actions and of their associated rewards Y1 , . . , Yn , grouped according to the arms as Xj,1 , . . , Xj,Tj (n) , for j = 1, . . , n Empirical distribution of plays (EDP) Draws a recommendation using the probability distribution ψn = Empirical best arm (EBA) Only considers arms j with Tj (n) μj,n 1 n n δIt . t=1 1, computes their associated empirical means 1 = Tj (n) Tj (n) Xj,s , s=1 and forms a deterministic recommendation (conditionally to the history), ψn = δJn∗ where Jn∗ ∈ argmax μj,n j (ties broken in some way).

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Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009, Proceedings by Ricard Gavaldà, Gabor Lugosi, Thomas Zeugmann, Sandra Zilles


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