1 citations · 1 across the 10 of their papers we have counts for
12 papers
Extended Mean Field Control Games with Moment Interactions: General Framework and Linear-Quadratic Model
Zhongyuan Cao, Mathieu Laurière, Andrew Shi +1
Mean field control games (MFCGs) provide a framework for studying non-cooperative games involving groups of cooperative players in the limit as both the number of players and the n…
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…
Controlling Exploration-Exploitation in GFlowNets via Markov Chain Perspectives
Lin Chen, Samuel Drapeau, Fanghao Shao +5
Generative Flow Network (GFlowNet) objectives implicitly fix an equal mixing of forward and backward policies, potentially constraining the exploration-exploitation trade-off durin…
Dynamic Programming Principle and Stabilization for Mean-Field Quantum Filtering Systems
Sofiane Chalal, Nina H. Amini, Hamed Amini +1
Working within the quantum filtering framework, we establish a dynamic programming principle in an infinite-dimensional setting by embedding the state space into the Hilbert-Schmid…
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…
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…
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…
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…