Generalization bounds via convex analysisSpeaker: Gabor Lugosi, Pompeu Fabra University, BarcelonaTime: Friday, Oct 7, 2022, 10:00AM - 11:00AM, Eastern Time Zoom Link: contact tml.online.seminars@gmail.com Abstract:Recent results bound the generalization error of supervised learning algorithms in terms of the mutual information between their input and the output. In this talk we present a framework to generalize this result beyond the standard choice of Shannon's mutual information . We show that it is indeed possible to replace the mutual information by any strongly convex function of the joint input-output distribution, combined with a bound on an appropriately chosen norm capturing the geometry of the dependence measure. This allows us to derive a variety of generalization bounds that are either new or strengthen previously known ones. This talk is based on joint work with Gergely Neu.Speaker's BioGabor Lugosi received his Ph.D. from the Hungarian Academy of Sciences in 1991. He an ICREA research professor at the Department of Economics, Pompeu Fabra University, Barcelona. His research interests include the theory of machine learning, combinatorial statistics, random structures, and information theory. |