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20242026
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math.PR2026

Almost stochastic dominance via optimal transport

Alfred Müller, Johannes Wiesel

The paper introduces a parametric family of almost stochastic dominance relations for probability distributions, characterizing the optimal γ parameter through an optimal transport…

math.PR2026

Adapted Wasserstein Barycenters of Gaussian Processes

Madhu Gunasingam, Francesco Mattesini, Johannes Wiesel +1

The paper investigates how to compute barycenters of filtered Gaussian processes using the adapted Wasserstein distance, providing existence results, decomposition into classical B…

math.PR2026

The fast rate of convergence of the smooth adapted Wasserstein distance

Martin Larsson, Jonghwa Park, Johannes Wiesel

Estimating a -dimensional distribution by the empirical measure of its samples is an important task in probability theory, statistics and machine learning. It is…

math.PR2025

Convergence of the adapted empirical measure for mixing observations

Ruslan Mirmominov, Johannes Wiesel

The adapted Wasserstein distance is a modification of the classical Wasserstein metric, that provides robust and dynamically consistent comparisons of laws of stocha…

math.PR2025

Dynamic characterization of barycentric optimal transport problems and their martingale relaxation

Ivan Guo, Severin Nilsson, Johannes Wiesel

We extend the Benamou-Brenier formula from classical optimal transport to weak optimal transport and show that the barycentric optimal transport problem studied by Gozlan and Juill…

math.PR2025

Empirical martingale projections via the adapted Wasserstein distance

Jose Blanchet, Johannes Wiesel, Erica Zhang +1

Given a collection of multidimensional pairs , we study the problem of projecting the associated suitably smoothed empirical measure onto the space of…