ASYMPTOTIC DISTRIBUTION AND CONVERGENCE RATES OF STOCHASTIC ALGORITHMS FOR ENTROPIC OPTIMAL TRANSPORTATION BETWEEN PROBABILITY MEASURES

• 成果类型: Article
• 署名作者: Bercu, Bernard; Bigot, Jeremie
• 署名单位: Universite de Bordeaux; Universite de Bordeaux; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS1987
• 发表日期: 2021
• 页码: 968-987
• 关键词: wasserstein; approximation; THEOREMS; tests

摘要

This paper is devoted to the stochastic approximation of entropically regularized-Wasserstein distances between two probability measures, also known as Sinkhorn divergences. The semi-dual formulation of such regularized optimal transportation problems can be rewritten as a nonstrongly concave optimisation problem. It allows to implement a Robbins-Monro stochastic algorithm to estimate the Sinkhorn divergence using a sequence of data sampled from one of the two distributions. Our main contribution is to establish the almost sure convergence and the asymptotic normality of a new recursive estimator of the Sinkhorn divergence between two probability measures in the discrete and semi-discrete settings. We also study the rate of convergence of the expected excess risk of this estimator in the absence of strong concavity of the objective function. Numerical experiments on synthetic and real datasets are also provided to illustrate the usefulness of our approach for data analysis.