3 papers
q-fin.RM2026
Adaptive Multilevel Stochastic Approximation of the Value-at-Risk
Stéphane Crépey, Noufel Frikha, Azar Louzi +1
Crépey, Frikha, and Louzi (2025) introduced a multilevel stochastic approximation scheme to compute the value-at-risk of a financial loss that is only simulatable by Monte Carlo.…
cs.LG2026
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Jonathan Spence, TobÃas I. Liaudat, Konstantinos Zygalakis +1
Markov chain Monte Carlo (MCMC) methods are fundamental to Bayesian computation, but can be computationally intensive, especially in high-dimensional settings. Push-forward generat…
cs.CV2025
Learning few-step posterior samplers by unfolding and distillation of diffusion models
Charlesquin Kemajou Mbakam, Jonathan Spence, Marcelo Pereyra
Diffusion models (DMs) have emerged as powerful image priors in Bayesian computational imaging. Two primary strategies have been proposed for leveraging DMs in this context: Plug-a…