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2013 Academic Year Seminars

Speaker(s): Nicolò Cesa-Bianchi
Organiser: Dr Craig J Saunders
Time: 06/04/2009 14:00-15:00
Location: B6/1083


In the online linear optimization problem a forecaster chooses, at each time instance, a vector x from a certain given subset S of the D-dimensional Euclidean space and suffers a time-dependent loss that is linear in x. The goal of the forecaster is to achieve that, in the long run, the accumulated loss is not much larger than that of the best possible vector in S. In this talk we consider the "bandit" setting of the linear optimization problem,in which the forecaster has only access to the losses of the chosen vectors. We survey some recent algorithms that solve this problem. For the special case in which S is a subset of the d-dimensional Boolean hypercube, we describe a new forecaster whose performance, for a variety of concrete choices of S, is better than all previously known bounds, and not improvable in general. We also point out that computationally efficient implementations for various interesting choices of S exist.

Joint work with Gabor Lugosi (Barcelona).

Speaker Biography

Nicolò Cesa-Bianchi

Università degli Studi di Milano

© School of Electronics and Computer Science of the University of Southampton