collaborators

5 papers

math.OC2026

Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data

Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1

This paper addresses model-free continuous-time mean-field control in a setting where the population dynamics evolve continuously according to an unknown McKean-Vlasov stochastic d…

math.OC2026

Policy Gradient for Continuous-Time Mean-Field Control

Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1

This paper develops a policy gradient method for entropy-regularized mean-field control in the discounted infinite-horizon setting. We consider randomized feedback policies and a c…

math.OC2026

Implicit Regularization of Large Neural Networks via Mean-Field Formulation

Beatrice Acciaio, Jakob Heiss, Gudmund Pammer +1

We propose a mathematical framework to explain implicit regularization from early stopping during the training of overparametrized neural networks. In the mean-field limit, the par…

math.OC2025

Iterative Schemes for Markov Perfect Equilibria

Felix Höfer, Mathieu Laurière, H. Mete Soner +1

We study Markov perfect equilibria in continuous-time dynamic games with finitely many symmetric players. The corresponding Nash system reduces to the Nash-Lasry-Lions equation for…

math.OC2025

Learning algorithms for mean field optimal control

H. Mete Soner, Josef Teichmann, Qinxin Yan

We analyze an algorithm to numerically solve the mean-field optimal control problems by approximating the optimal feedback controls using neural networks with problem specific arch…