Pair-matching and sequential learning of communities
We will discuss the question of learning sequentially successful matching between individuals. We will consider the simplest settings and we will exhibit some phase transition phenomenas. The analysis will rely on some recent results on community recovery in stochastic block models, that we will introduce, starting from the most basic settings.
Christophe Giraud received a Ph.D in probability theory from the university Paris 6. He was assistant professor at the university of Nice from 2002 to 2008. He has been associate professor at the école polytechnique since 2008 and Professor at Paris Sud university (Orsay) since 2012. His recent work focus on the understanding of some fundamental problems in statistics, the analysis of some popular algorithm in Machine Learning and the design of some new ones.
2019-04-09 at 3:00 pm (subject to variability)