The statistical effect of entropic regularization in optimal transportation

Abstract

We propose to tackle the problem of understanding the effect of regularization in Sinkhorn algotihms. In the case of Gaussian distributions we provide a closed form for the regularized optimal transport which enables to provide a better understanding of the effect of the regularization from a statistical framework.

References in corpus (7)

• Fast Computation of Wasserstein Barycenters
• Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
• Stochastic Optimization for Large-scale Optimal Transport
• Sample Complexity of Sinkhorn divergences
• Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
• Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
• Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

Cited by in corpus (5)

• Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
• Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes
• Conditional Wasserstein Barycenters and Interpolation/Extrapolation of Distributions
• Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings
• Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals