We consider the fundamental problem of sampling the optimal transport coupling between
given source and target distributions. In certain cases, the optimal transport plan takes the …

Abstract Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks,
starting from easy ones and gradually learning towards difficult tasks. In this work, we focus …

We study first-order optimization algorithms for computing the barycenter of Gaussian
distributions with respect to the optimal transport metric. Although the objective is …

Wasserstein Barycenter is a principled approach to represent the weighted mean of a given
set of probability distributions, utilizing the geometry induced by optimal transport. In this …

Wasserstein barycenters provide a geometric notion of the weighted average of probability
measures based on optimal transport. In this paper, we present a scalable algorithm to …

Wasserstein barycenters have become popular due to their ability to represent the average
of probability measures in a geometrically meaningful way. In this paper, we present an …

This paper focuses on computing the convex conjugate operation that arises when solving
Euclidean Wasserstein-2 optimal transport problems. This conjugation, which is also …

We study the use of amortized optimization to predict optimal transport (OT) maps from the
input measures, which we call Meta OT. This helps repeatedly solve similar OT problems …

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …