97 citations · 798 across the 62 of their papers we have counts for
82 papers
Generative modeling for time series via Schr{ö}dinger bridge
Mohamed Hamdouche, Pierre Henry-Labordere, Huyên Pham
We propose a novel generative model for time series based on Schr{ö}dinger bridge (SB) approach. This consists in the entropic interpolation via optimal transport between a referen…
Actor-Critic learning for mean-field control in continuous time
Noufel Frikha, Maximilien Germain, Mathieu Laurière +2
We study policy gradient for mean-field control in continuous time in a reinforcement learning setting. By considering randomised policies with entropy regularisation, we derive a…
Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing
Mohamed Hamdouche, Pierre Henry-Labordere, Huyen Pham
We develop policy gradients methods for stochastic control with exit time in a model-free setting. We propose two types of algorithms for learning either directly the optimal polic…
Mean-field neural networks: learning mappings on Wasserstein space
Huyên Pham, Xavier Warin
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/c…
Quantitative propagation of chaos for mean field Markov decision process with common noise
Médéric Motte, Huyên Pham
We investigate propagation of chaos for mean field Markov Decision Process with common noise (CMKV-MDP), and when the optimization is performed over randomized open-loop controls o…
Differential learning methods for solving fully nonlinear PDEs
William Lefebvre, Grégoire Loeper, Huyên Pham
We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First the…