From the 2 of 10 linked papers with an AI index.
10 papers
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…
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…
Dependence Measures via Adapted Optimal Transport: Stability and Rates of Convergence
Jonathan Ansari, Johannes Wiesel
Recently studied dependence measures, such as Chatterjee's rank correlation, that characterize both independence and perfect functional dependence, provide a powerful framework for…
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…
Sample complexity for divergence regularized optimal transport with radial cost
Ruiyu Han, Johannes Wiesel
We prove a new sample complexity result for divergence regularized optimal transport. Our bound holds for probability measures on~ with exponential tail decay and for…
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…