Get Algorithmic Learning Theory: 20th International Conference, PDF

By Sanjoy Dasgupta (auth.), Ricard Gavaldà , Gábor Lugosi, Thomas Zeugmann, Sandra Zilles (eds.)

ISBN-10: 3642044131

ISBN-13: 9783642044137

This booklet constitutes the refereed court cases of the 20 th 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 provided including the abstracts of five invited talks have been rigorously 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 comprises abstracts of the

invited talks: Sanjoy Dasgupta, the 2 Faces of energetic studying; Hector Geffner, Inference and

Learning in making plans; Jiawei Han, Mining Heterogeneous; info 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

The sequence (ϕt ) is referred to as an allocation strategy. The primary task is to output at the end of each round t a recommendation ψt ∈ P{1, . . , K} to be used to form a randomized play in a one-shot instance if/when the environment sends some stopping signal meaning that the exploration phase is over. The sequence (ψt ) is referred to as a recommendation strategy. Figure 1 summarizes the description of the sequential game and points out that the information available to the forecaster for choosing ϕt , respectively ψt , is formed by the Xj,s for j = 1, .

UCB(p) — Plays each arm once and then the one with the best upper confidence bound Parameter: quantile factor p For rounds t = 1, . . , K, play ϕt = δt For each round t = K + 1, K + 2, . . , (1) compute, for all j = 1, . . , K, the quantities μj,t−1 = 1 Tj (t − 1) Tj (t−1) Xj,s ; s=1 p ln(t − 1) Tj (t − 1) (ties broken by choosing, for instance, the arm with smallest index). ,K Fig. 2. Two allocation strategies Table 1. Distribution-dependent (top) and distribution-free (bottom) bounds on the expected simple regret of the considered pairs of allocation (lines) and recommendation (columns) strategies.

Typical good strategies, like the UCB strategies of [ACBF02], trade off between exploration and exploitation. Our setting is as follows. The forecaster may sample the arms a given number of times n (not necessarily known in advance) and is then asked to output a recommendation, formed by a probability distribution over the arms. He is evaluated by his simple regret, that is, the difference between the average payoff of the best arm and the average payoff obtained by his recommendation. The distinguishing feature from the classical multi-armed bandit problem is that the exploration phase and the evaluation phase are separated.

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Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings by Sanjoy Dasgupta (auth.), Ricard Gavaldà , Gábor Lugosi, Thomas Zeugmann, Sandra Zilles (eds.)


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