A Fisher-Rao Gradient Flow for Entropic Mean-Field Min-Max Games
Abstract
Gradient flows play a substantial role in addressing many machine learning problems. We examine the convergence in continuous-time of a Fisher-Rao (Mean-Field Birth-Death) gradient flow in the context of solving convex-concave min-max games with entropy regularization. We propose appropriate Lyapunov functions to demonstrate convergence with explicit rates to the unique mixed Nash equilibrium.
Cite
Text
Lascu et al. "A Fisher-Rao Gradient Flow for Entropic Mean-Field Min-Max Games." Transactions on Machine Learning Research, 2024.Markdown
[Lascu et al. "A Fisher-Rao Gradient Flow for Entropic Mean-Field Min-Max Games." Transactions on Machine Learning Research, 2024.](https://mlanthology.org/tmlr/2024/lascu2024tmlr-fisherrao/)BibTeX
@article{lascu2024tmlr-fisherrao,
title = {{A Fisher-Rao Gradient Flow for Entropic Mean-Field Min-Max Games}},
author = {Lascu, Razvan-Andrei and Majka, Mateusz B. and Szpruch, Lukasz},
journal = {Transactions on Machine Learning Research},
year = {2024},
url = {https://mlanthology.org/tmlr/2024/lascu2024tmlr-fisherrao/}
}