2 papers
cs.LG2026
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2
Diffusion-based posterior samplers use pretrained diffusion priors to sample from measurement- or reward-conditioned posteriors, and are widely used for inverse problems. Yet their…
cs.LG2025
Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
Advait Parulekar, Litu Rout, Karthikeyan Shanmugam +1
We study the problem of posterior sampling in the context of score based generative models. We have a trained score network for a prior , a measurement model , and ar…