Technion, IEM faculty - Statistics Seminar Speaker: Elad Hazan Title: Interpolating worst-case and statistical learning Date: 06/03/2011 Time: 14:30 Place: Bloomfield-527 Abstract: <http://ie.technion.ac.il/seminar_files/1295953553_Hazan.pdf> See also here: The sequential decision problem is a fundamental pillar of statistics which has been studied since the seminal work of Hannan and Robins in the fifties, with recent invigorated interest in the field of machine learning. This framework is more general, but usually provides weaker convergence guarantees than those attained in statistical learning theory. In 2005 Cesa-Bianchi, Mansour and Stoltz conjectured that the regret of sequential decision making algorithms can be bounded by the variation in the observed data, thereby providing a bridge between the two fields. We describe a solution to this conjecture in the fundamental setting of discrete decision making (the so called "experts problem"), and proceed to more general results along the same lines applied to decisions with partial information and to portfolio selection. Based on joint work with Satyen Kale, published in COLT09, SODA09, NIPS09, and Machine Learning Journal/JMLR. --------------------------------------------------------- Technion Math. Net (TECHMATH) Editor: Michael Cwikel <techm@math.technion.ac.il> Announcement from: <ynardi@ie.technion.ac.il>