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.ML2026
On Forgetting and Stability of Score-based Generative models
Stanislas Strasman, Gabriel Cardoso, Sylvain Le Corff +2
Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provides quantitative bounds on the sampling…
math.ST2025
Wasserstein Convergence of Critically Damped Langevin Diffusions
Stanislas Strasman, Sobihan Surendran, Claire Boyer +3
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications and benefit from strong theoretical guarantees. Rec…