By Ricard Gavaldà, Gabor Lugosi, Thomas Zeugmann, Sandra Zilles
This e-book constitutes the refereed complaints of the 20 th overseas convention on Algorithmic studying concept, ALT 2009, held in Porto, Portugal, in October 2009, co-located with the twelfth overseas 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, lively 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 lively studying; Hector Geffner, Inference and studying in making plans; Jiawei Han, Mining Heterogeneous; info Networks through Exploring the ability of hyperlinks, Yishay Mansour, studying and area variation; Fernando C.N. Pereira, studying on the net.
Read Online or Download Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009, Proceedings PDF
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Additional resources for Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009, Proceedings
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Online optimization in X – armed bandits. In: Advances in Neural Information Processing Systems, vol. : Bandit algorithms for tree search. : PAC bounds for multi-armed bandit and Markov decision processes. H. ) COLT 2002. LNCS (LNAI), vol. 2375, pp. 255–270. : Modification of UCT with patterns in Monte-Carlo go. : Nearly tight bounds for the continuum-armed bandit problem. : Bandit based Monte-carlo planning. , Spiliopoulou, M. ) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. : Asymptotically efficient adaptive allocation rules.
N ≤0 (3) almost surely, where s1:T is its random cumulative loss. In this section we study the asymptotical consistency of probabilistic learning algorithms in the case of unbounded one-step losses. Notice that when 0 ≤ sit ≤ 1 all expert algorithms have total loss ≤ T on first T steps. This is not true for the unbounded case, and there are no reasons to divide the expected regret (2) by T . We change the standard time scaling (2) and (3) on a new scaling based on a new notion of volume of a game.
For moderate values of n, strategies not pulling each arm a linear number of the times in the exploration phase can have interesting simple regrets. To do so, we consider only two natural and well-used allocation strategies. The first one is the uniform allocation, which we use as a simple benchmark; it pulls each arm a linear number of times. , its minimizes the cumulative regret) and pulls suboptimal arms a logarithmic number of times only. Of course, fancier allocation strategies should also be considered in a second time but since the aim of this paper is to study the links between cumulative and simple regrets, we restrict our attention to the two discussed above.