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arXiv · 2206.13472

On the sample complexity of entropic optimal transport

Philippe Rigollet·Austin J. Stromme
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Abstract

We study the sample complexity of entropic optimal transport in high dimensions using computationally efficient plug-in estimators. We significantly advance the state of the art by establishing dimension-free, parametric rates for estimating various quantities of interest, including the entropic regression function which is a natural analog to the optimal transport map. As an application, we propose a practical model for transfer learning based on entropic optimal transport and establish parametric rates of convergence for nonparametric regression and classification.

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Philippe Rigollet, Austin J. Stromme. 2022-06-27. On the sample complexity of entropic optimal transport. https://arxiv.org/abs/2206.13472

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