Optimal Transport

Optimal transport (OT) is a mathematical framework for efficiently moving probability distributions from one configuration to another while minimizing a cost function, often visualized as the "earth mover's distance." Current research focuses on developing robust and scalable OT algorithms, particularly for high-dimensional data and applications involving noisy or unbalanced distributions, often employing neural networks and Sinkhorn iterations. These advancements are significantly impacting diverse fields, including machine learning (e.g., generative modeling, domain adaptation), image processing, and even economic modeling, by providing powerful tools for data analysis, representation learning, and fair data manipulation.