5 papers
From Saddle Points Toward Global Minima: A Newton-Type Method on Wasserstein Space
Razvan-Andrei Lascu, Taiji Suzuki
We study the minimization of non-convex functionals over the Wasserstein space. While recent work has showed that perturbed Wasserstein gradient methods can avoid saddle points for…
PPO in the Fisher-Rao geometry
Razvan-Andrei Lascu, David Šiška, Łukasz Szpruch
Proximal Policy Optimization (PPO) is widely used in reinforcement learning due to its strong empirical performance, yet it lacks formal guarantees for policy improvement and conve…
Non-convex entropic mean-field optimization via Best Response flow
Razvan-Andrei Lascu, Mateusz B. Majka
We study the problem of minimizing non-convex functionals on the space of probability measures, regularized by the relative entropy (KL divergence) with respect to a fixed referenc…
Linear convergence of proximal descent schemes on the Wasserstein space
Razvan-Andrei Lascu, Mateusz B. Majka, David Šiška +1
We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on t…
A Fisher-Rao gradient flow for entropic mean-field min-max games
Razvan-Andrei Lascu, Mateusz B. Majka, Łukasz Szpruch
Gradient flows play a substantial role in addressing many machine learning problems. We examine the convergence in continuous-time of a \textit{Fisher-Rao} (Mean-Field Birth-Death)…