Jiequn Han

Self-Consistent Interpolants Learn Clean Priors From Corrupted Data

Jiequn Han’s talk argues that transport-based generative models should be treated not only as tools for sampling clean data distributions, but as machinery for recovering and adapting those distributions when the usual clean training set is absent. His main proposal, Self-Consistent Stochastic Interpolants, learns a clean prior from corrupted observations by iterating a transport map until the learned distribution, passed through a trusted forward simulator, reproduces the observed data. Han presents the method as a black-box alternative to EM-style inverse generative modeling, with the caveat that simulator mismatch remains a central unresolved risk.