5 papers
BAM! Bayesian Anything Model: a foundation model for generative computational imaging
Alessio Spagnoletti, Charlesquin Kemajou Mbakam, Jonathan Spence +2
Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls into two camps. Large foundatio…
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Jonathan Spence, Tobías 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…
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…
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. T…
Nested Multilevel Monte Carlo with Biased and Antithetic Sampling
Abdul-Lateef Haji-Ali, Jonathan Spence
We consider the problem of estimating a nested structure of two expectations taking the form , where . Terms of this form arise…