Characterization of Law-Invariant Coherent Risk Measures Through Optimal Transport

Title

Characterization of Law-Invariant Coherent Risk Measures Through Optimal Transport

Speaker

Riccardo Bonalli - CNRS, CentraleSupélec (FR)

Abstract

In this talk, I propose a novel characterization of law-invariant and coherent risk measures. The latter, which is closely related to Kusuoka's representation theorem, is based on a particular optimal transport problem in which the second marginal of the admissible plans is not fixed, but required to lie within a target set of probability measures. This allows among other things to derive general duality formulas in a systematic fashion. If time allows, I will show how such a characterization of law-invariant and coherent risk measures may be leveraged to unveil Markov-type regularity of epsilon-optimal solutions to very general risk-averse optimal control problems.

Bio

Riccardo Bonalli obtained his PhD in applied mathematics from Sorbonne Universite, France in 2018, in collaboration with ONERA-The French Aerospace Lab. From 2018 to 2021, he was a postdoctoral researcher with the Department of Aeronautics and Astronautics, Stanford University. Since 2021, Riccardo is a tenured CNRS researcher with the Laboratory of Signals and Systems (L2S), CNRS and Université Paris-Saclay, France. His research interests concern theoretical and numerical robust optimal control with techniques from differential geometry, statistical analysis, and machine learning, and applications in aerospace systems and robotics. He is a recipient of the ONERA Best PhD Student Award 2018 in Systems and Control, the Control Systems Magazine Outstanding Paper Award 2023, and the IEEE CDC 2023 Outstanding Student Paper Award (as advisor).

When

Friday, April 17th, 12:00

Where

Room 322, UniGe DIBRIS/DIMA, Via Dodecaneso 35