A Fisher-Rao gradient flow for entropic mean-field min-max games
<|reference_start|>A Fisher-Rao gradient flow for entropic mean-field min-max games: 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) 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.<|reference_end|>
@article{lascu2024a,
    title={A Fisher-Rao gradient flow for entropic mean-field min-max games},
    author={Razvan-Andrei Lascu, Mateusz B. Majka, {L}ukasz Szpruch},
    journal={Transactions on Machine Learning Research (2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15834},
    primaryClass={math.OC cs.LG math.PR}
}