Statistical Optimal Transport arxiv:2407.18163v2 [math.ST] 7 Nov 2024 Page 1 Statistical
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …

Estimation of Wasserstein distances in the Spiked Transport Model Page 1 Bernoulli 28(4),
2022, 2663–2688 https://doi.org/10.3150/21-BEJ1433 Estimation of Wasserstein distances …

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

In the context of regression, we consider the fundamental question of making an estimator
fair while preserving its prediction accuracy as much as possible. To that end, we define its …

We present a framework for performing regression when both covariate and response are
probability distributions on a compact interval. Our regression model is based on the theory …

We study a general formulation of regularized Wasserstein barycenters that enjoys favorable
regularity, approximation, stability and (grid-free) optimization properties. This barycenter is …

The optimal transport map between the standard Gaussian measure and an α-strongly log-
concave probability measure is α− 1/2-Lipschitz, as first observed in a celebrated theorem of …

In this work we introduce the concept of Bures–Wasserstein barycenter Q∗, that is
essentially a Fréchet mean of some distribution P supported on a subspace of positive semi …

We study decision problems under uncertainty, where the decision-maker has access to $ K
$ data sources that carry {\em biased} information about the underlying risk factors. The …

Graph size generalization is hard for Message passing neural networks (MPNNs). The
graph-level classification performance of MPNNs degrades across various graph sizes …