1 citations · 1 across the 3 of their papers we have counts for
4 papers
Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems
Jingguo Zhang, Xianjin Yang, Chenchen Mou +1
Mean field games (MFGs) describe the collective behavior of large populations of interacting agents. In this work, we tackle ill-posed inverse problems in potential MFGs, aiming to…
Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample size
Huafu Liao, Alpár R. Mészáros, Chenchen Mou +1
This paper deals with a class of neural SDEs and studies the limiting behavior of the associated sampled optimal control problems as the sample size grows to infinity. The neural S…
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes
Jinyan Guo, Chenchen Mou, Xianjin Yang +1
This paper presents a Gaussian Process (GP) framework, a non-parametric technique widely acknowledged for regression and classification tasks, to address inverse problems in mean f…
Linear-Quadratic Mean Field Control with Non-Convex Data
Mengzhen Li, Chenchen Mou, Zhen Wu +1
In this manuscript, we study a class of linear-quadratic (LQ) mean field control problems with a common noise and their corresponding -particle systems. The mean field control p…