Postdoc, University of California, Berkeley
2 papers at NeurIPS 2025
This paper introduces Proximal Diffusion Models (ProxDM), derived from backward discretization of an SDE and based on learned proximal operators, achieving provable faster sampling complexity and empirically much faster convergence.
We build a nearly-perfect predictive model for memorization in diffusion models using theory and controlled experiments.
