paper-conference

We propose a scalable method to approximate diffusion processes via Wasserstein-2 gradient flows empowered by input-convex neural networks and Jordan-Kinderlehrer-Otto time discretization.

We introduce a topology-based domain agnostic methodology for comparing data manifolds.

We present a new algorithm to compute Wasserstein-2 barycenters of continuous distributions powered by a straightforward optimization procedure without introducing bias or a generative model.

We present a new end-to-end algorithm to compute optimal transport maps between continuous distributions without introducing bias or resorting to minimax optimization.