We propose a novel neural algorithm for the fundamental problem of computing the entropic
optimal transport (EOT) plan between probability distributions which are accessible by …

Particle-based deep generative models, such as gradient flows and score-based diffusion
models, have recently gained traction thanks to their striking performance. Their principle of …

Normalizing flow is a class of deep generative models for efficient sampling and likelihood
estimation, which achieves attractive performance, particularly in high dimensions. The flow …

Gradient flows are a powerful tool for optimizing functionals in general metric spaces,
including the space of probabilities endowed with the Wasserstein metric. A typical …

We propose a sampling algorithm that achieves superior complexity bounds in all the
classical settings (strongly log-concave, log-concave, Logarithmic-Sobolev inequality (LSI) …

Neural optimal transport techniques mostly use Euclidean cost functions, such as $\ell^ 1$
or $\ell^ 2$. These costs are suitable for translation tasks between related domains, but they …

We propose conditional flows of the maximum mean discrepancy (MMD) with the negative
distance kernel for posterior sampling and conditional generative modeling. This MMD …

Particle-based variational inference methods (ParVIs) such as Stein variational gradient
descent (SVGD) update the particles based on the kernelized Wasserstein gradient flow for …

Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-
smooth Riesz kernels show a rich structure as singular measures can become absolutely …

We present a discretization-free scalable framework for solving a large class of mass-
conserving partial differential equations (PDEs), including the time-dependent Fokker …