Wasserstein Distributionally Robust Optimization - Theory and Applications in Machine Learning

Wasserstein Distributionally Robust Optimization - Theory and Applications in Machine Learning

Explore the theory and applications of Wasserstein Distributionally Robust Optimization in machine learning through this comprehensive lecture. Delve into data-driven decision-making challenges, learn about the Wasserstein distance approach, and discover its benefits in solving complex problems. Examine the connections between statistical learning and Wasserstein DRO, and understand its applications in classification, regression, maximum likelihood estimation, and minimum mean square error estimation. Gain insights into tractable convex optimization problems, out-of-sample guarantees, and asymptotic consistency in decision-making under uncertainty.