High Dimensional Probability VIII

This volume collects selected papers from the 8th High Dimensional Probability meeting held at Casa Matemática Oaxaca (CMO), Mexico.

High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, random graphs, information theory and convex geometry.

The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques and perspectives to analyze random phenomena.

• high dimensional probability
• limit theorems
• infinite-dimensional spaces
• Hilbert spaces
• Banach spaces
• random matrices
• nonparametric statistics
• empirical processes
• functional estimation
• random graphs
• convex geometry

• MAP 5, Université Paris Descartes, Paris, France

  Nathael Gozlan

• Institute of Mathematics, University of Warsaw, Warsaw, Poland

  Rafał Latała

• Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France

  Karim Lounici

• Department of Mathematical Sciences, University of Delaware, Newark, USA

  Mokshay Madiman

Policies and ethics