Wasserstein Gradient Flows and Applications to Sampling in Machine Learning - Lecture 1

Explore the fundamental concepts of Wasserstein gradient flows and their applications to sampling in machine learning in this comprehensive lecture by Anna Korba. Delve into the intricate mathematical theories presented during the thematic meeting "Frontiers in interacting particle systems, aggregation-diffusion equations & collective behavior" at the Centre International de Rencontres Mathématiques in Marseille, France. Gain insights into the intersection of probability theory, optimization, and machine learning as Korba elucidates the connections between Wasserstein geometry and sampling algorithms. Access this enriched video content through CIRM's Audiovisual Mathematics Library, featuring chapter markers, keywords, abstracts, bibliographies, and Mathematics Subject Classification for enhanced navigation and understanding. Utilize the multi-criteria search function to explore related talks by worldwide mathematicians and broaden your knowledge in this cutting-edge field of mathematical research.