Concentration Inequalities and The Entropy Method

2013 ISIT Plenary Lecture
Concentration Inequalities and The Entropy Method
Gábor Lugosi
Department of Economics, Pompeu Fabra University


In this talk we discuss concentration inequalities that estimate deviations of functions of independent random variables from their expectation. Such inequalities often serve as an elegant and powerful tool and have countless applications. Various methods have been developed for proving such inequalities, such as martingale methods, Talagrand's induction method, or Marton's transportation-of-measure technique. In this talk we focus on the so-called entropy method, pioneered by Michel Ledoux, that is based on some simple information-theoretic inequalities. We present the main steps of the proof technique and discuss various inequalities and some applications.


Gábor Lugosi graduated in electrical engineering at the Technical University of Budapest in 1987, and received his Ph.D. from the Hungarian Academy of Sciences in 1991. Since 1996, he has been at the Department of Economics, Pompeu Fabra University. In 2006 he became an ICREA research professor. His research interest includes learning theory, nonparametric statistics, inequalities in probability, random structures, and information theory.


ISIT Plenary Lecture