A Survey on Optimal Transport for Machine Learning: Theory and Applications

Understand

Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning.

• It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions.
• In this survey, we present a brief introduction and history, a survey of previous work and propose directions of future study.
• We will begin by looking at the history of optimal transport and introducing the founders of this field.