8 papers · 1 filter
Scalable method for mean field control with kernel interactions via random Fourier features
Zhongyuan Cao, Kaustav Das, Nicolas Langrené +1
We develop a scalable algorithm for mean field control problems with kernel interactions by combining particle system simulations with random Fourier feature approximations. The me…
Dual Approaches to Stochastic Control via SPDEs and the Pathwise Hopf Formula
Mathieu Laurière, Jiefei Yang
We develop dual approaches for continuous-time stochastic control problems, enabling the computation of robust dual bounds in high-dimensional state and control spaces. Building on…
Discrete-Time Mean Field Type Games: Probabilistic Setup
Grégoire Lambrecht, Mathieu Laurière
We introduce a general probabilistic framework for discrete-time, infinite-horizon discounted Mean Field Type Games (MFTGs) with both global common noise and team-specific common n…
Deep Learning for the Multiple Optimal Stopping Problem
Mathieu Laurière, Mehdi Talbi
This paper presents a novel deep learning framework for solving multiple optimal stopping problems in high dimensions. While deep learning has recently shown promise for single sto…
Probabilistic Analysis of Graphon Mean Field Control
Zhongyuan Cao, Mathieu Laurière
Motivated by recent interest in graphon mean field games and their applications, this paper provides a comprehensive probabilistic analysis of graphon mean field control (GMFC) pro…
Deep Signature Approach for McKean-Vlasov FBSDEs in a Random Environment
Ruimeng Hu, Botao Jin, Mathieu Laurière +1
Mean-field games with common noise provide a powerful framework for modeling the collective behavior of large populations subject to shared randomness, such as systemic risk in fin…