Optimal Transport for Machine Learners

The article presents a comprehensive overview of optimal transport (OT) techniques tailored for machine learning applications, emphasizing their importance in comparing probability measures such as empirical datasets and latent distributions. Key topics include the mathematical foundations of OT, algorithmic implementations like linear programming and Sinkhorn scaling, and their relevance to modern ML tasks such as generative modeling, domain adaptation, and robust learning. This work aims to equip practitioners with the necessary mathematical and computational tools to effectively integrate OT into their machine learning workflows.