paper-conference

We present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model and construct Ave, celeba! dataset which can be used for quantitative evaluation of barycenter algorithms.

While optimal transport cost serves as the loss for popular generative models, we demonstrate that the optimal transport map can be used as the generative model itself.

We construct pairs of continuous measures with analytically-known optimal transport (OT) solutions for the quadratic cost to test existing parametric OT solvers including those used in implicit generative modeling.

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.