Tag: wasserstein

  • Accelerated Regularized Wasserstein Proximal Sampling Algorithms

    Accelerated Regularized Wasserstein Proximal Sampling Algorithms arXiv:2601.09848v1 Announce Type: new Abstract: We consider sampling from a Gibbs distribution by evolving a finite number of particles using a particular score estimator rather than Brownian motion. To accelerate the particles, we consider a second-order score-based ODE, similar to Nesterov acceleration. In contrast to traditional kernel density score…

  • Neural Local Wasserstein Regression

    Neural Local Wasserstein Regression arXiv:2511.10824v1 Announce Type: new Abstract: We study the estimation problem of distribution-on-distribution regression, where both predictors and responses are probability measures. Existing approaches typically rely on a global optimal transport map or tangent-space linearization, which can be restrictive in approximation capacity and distort geometry in multivariate underlying domains. In this paper,…

  • Minimax-Optimal Two-Sample Test with Sliced Wasserstein

    Minimax-Optimal Two-Sample Test with Sliced Wasserstein arXiv:2510.27498v1 Announce Type: new Abstract: We study the problem of nonparametric two-sample testing using the sliced Wasserstein (SW) distance. While prior theoretical and empirical work indicates that the SW distance offers a promising balance between strong statistical guarantees and computational efficiency, its theoretical foundations for hypothesis testing remain limited.…

  • Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances

    Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances arXiv:2509.20508v1 Announce Type: new Abstract: We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein (SW) distances. Specifically, we…

  • Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance

    Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance arXiv:2509.10166v1 Announce Type: new Abstract: In this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are general, our guiding thread is the sliced Wasserstein…

  • An in depth look at the Procrustes-Wasserstein distance: properties and barycenters

    An in depth look at the Procrustes-Wasserstein distance: properties and barycenters arXiv:2507.00894v1 Announce Type: new Abstract: Due to its invariance to rigid transformations such as rotations and reflections, Procrustes-Wasserstein (PW) was introduced in the literature as an optimal transport (OT) distance, alternative to Wasserstein and more suited to tasks such as the alignment and comparison…

  • On the Wasserstein Geodesic Principal Component Analysis of probability measures

    On the Wasserstein Geodesic Principal Component Analysis of probability measures arXiv:2506.04480v1 Announce Type: new Abstract: This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesic curves in the space of probability measures that best capture the modes of variation of…

  • Procrustes Wasserstein Metric: A Modified Benamou-Brenier Approach with Applications to Latent Gaussian Distributions

    Procrustes Wasserstein Metric: A Modified Benamou-Brenier Approach with Applications to Latent Gaussian Distributions arXiv:2503.16580v1 Announce Type: new Abstract: We introduce a modified Benamou-Brenier type approach leading to a Wasserstein type distance that allows global invariance, specifically, isometries, and we show that the problem can be summarized to orthogonal transformations. This distance is defined by penalizing…

  • Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization

    Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization arXiv:2501.14635v1 Announce Type: new Abstract: The optimal transport barycenter (a.k.a. Wasserstein barycenter) is a fundamental notion of averaging that extends from the Euclidean space to the Wasserstein space of probability distributions. Computation of the unregularized barycenter for discretized probability distributions on point clouds is a challenging task when…