4 papers
Model-free policy gradient for discrete-time mean-field control
Matthieu Meunier, Huyên Pham, Christoph Reisinger
We study model-free policy learning for discrete-time mean-field control (MFC) problems with finite state space and compact action space. In contrast to the extensive literature on…
Weighted Conditional Flow Matching
Sergio Calvo-Ordonez, Matthieu Meunier, Alvaro Cartea +3
Conditional flow matching (CFM) has emerged as a powerful framework for training continuous normalizing flows due to its computational efficiency and effectiveness. However, standa…
Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Matthieu Meunier, Christoph Reisinger, Yufei Zhang
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental chall…
Partially Stochastic Infinitely Deep Bayesian Neural Networks
Sergio Calvo-Ordonez, Matthieu Meunier, Francesco Piatti +1
In this paper, we present Partially Stochastic Infinitely Deep Bayesian Neural Networks, a novel family of architectures that integrates partial stochasticity into the framework of…