Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals

Abstract: \emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability measures. OT unfortunately suffers from the curse of dimensionality and requires regularization for practical computations, of which the \emph{entropic regularization} is a popular choice, which can be 'unbiased', resulting in a \emph{Sinkhorn divergence}. In this work, we study the convergence of estimating the 2-Sinkhorn divergence between \emph{Gaussian processes} (GPs) using their finite-dimensional marginal distributions. We show almost sure convergence of the divergence when the marginals are sampled according to some base measure. Furthermore, we show that using $n$ marginals the estimation error of the divergence scales in a dimension-free way as $$\mathcal{O}\left(\epsilon^ {-1}n<sup>{-\frac{1}{2}}\right)$$, where $\epsilon$ is the magnitude of entropic regularization.