no code implementations • 23 Aug 2023 • Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri
Our study provides a rigorous analysis, yielding simple, improved and sharp convergence bounds in KL applicable to any data distribution with finite Fisher information with respect to the standard Gaussian distribution.
no code implementations • 13 Apr 2023 • Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus
Our approach is novel in that it is purely probabilistic and relies on coupling by reflection techniques for controlled diffusions on the torus.
no code implementations • 12 Feb 2023 • Julien Claisse, Giovanni Conforti, Zhenjie Ren, SongBo Wang
In this paper by adding the Fisher Information as the regularizer, we relate the regularized mean field optimization problem to a so-called mean field Schrodinger dynamics.
no code implementations • 6 Apr 2020 • Giovanni Conforti, Anna Kazeykina, Zhenjie Ren
As applications, the dynamic games can be treated as games on a random environment when one treats the time horizon as the environment.
