Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey

arXiv:2305.05080v2 Announce Type: replace
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