Chaque session du séminaire est divisée en deux présentations scientifiques de 40 minutes chacune : 30 minutes d’exposé et 10 minutes de questions. François Lanusse & Johannes Hertrich animeront la session d'avril 2025 !
Inscriptions gratuites mais obligatoires, dans la limite des places disponibles. Un buffet sera servi à l'issue du séminaire.
Résumé
TBA
Résumé
In order to sample from an unnormalized probability density function, we propose to combine continuous normalizing flows (CNFs) with rejection-resampling steps based on importance weights. We relate the iterative training of CNFs with regularized velocity fields to a proximal mappings in the Wasserstein space. The alternation of local flow steps and non-local rejection-resampling steps allows to overcome local minima and mode collapse for multimodal distributions. The arising model can be trained iteratively, reduces the reverse Kulback-Leibler (KL) loss function in each step, allows to generate iid samples and moreover allows for evaluations of the generated underlying density. Numerical examples demonstrate the efficiency of our approach.