3 papers
stat.ML2026
Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2
Sequential Monte Carlo (SMC) methods are a natural tool for post-hoc conditioning of pretrained generative models, but in many applications the mutation kernels used by the particl…
stat.ME2026
Entropic Mirror Monte Carlo
Anas Cherradi, Yazid Janati, Alain Durmus +3
Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the targe…
stat.ME2026
Efficient Online Variational Estimation via Monte Carlo Sampling
Mathis Chagneux, Mathias Müller, Pierre Gloaguen +2
This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradien…