7 papers
Clustering in Deep Stochastic Transformers
Lev Fedorov, Michaël E. Sander, Romuald Elie +2
Transformers have revolutionized deep learning across various domains but understanding the precise token dynamics remains a theoretical challenge. Existing theories of deep Transf…
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…
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…
An Overview of Some Extensions of Mean Field Games beyond Perfect Homogeneity and Anonymity
Mathieu Laurière
The mean field games (MFG) paradigm was introduced to provide tractable approximations of games involving very large populations. The theory typically rests on two key assumptions:…
Robust mean-field control under common noise uncertainty
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
We propose and analyze a framework for discrete-time robust mean-field control problems under common noise uncertainty. In this framework, the mean-field interaction describes the…