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Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
March 25, 2024, 4:42 a.m. | Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek, Vivien Rolland, Lars Petersson
cs.LG updates on arXiv.org arxiv.org
Abstract: Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied questions. The last decade has been a witness to the remarkable contributions of this classical optimization problem to machine learning. This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport. We provide a …
arxiv cs.ai cs.lg machine machine learning scalable survey transport type
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