Dynamic Measure Transport Needs New Rules for Density-Driven Sampling
Aimee Maurais argues that dynamic measure transport, now central to diffusion models and flow matching, needs different design principles when the target distribution is specified by densities, likelihoods, or prior samples rather than training data. In a Microsoft Research seminar, she presents three lines of work toward that goal: gradient-free particle dynamics using likelihood evaluations, PDE-constrained path design to avoid unstable interpolations, and localized transport velocities that exploit conditional-independence structure in high-dimensional Bayesian and data-assimilation problems.
Microsoft Research·May 26, 2026·17 min read
