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Full Description
This monograph aims to offer a concise introduction to optimal transport, quickly transitioning to its applications in statistics and machine learning. It is primarily tailored for students and researchers in these fields, yet it remains accessible to a broader audience of applied mathematicians and computer scientists. Each chapter is complemented with exercises for the reader to test their understanding. As such, this monograph is suitable for a graduate course on the topic of statistical optimal transport.
Contents
1. Optimal Transport.- 2. Estimation of Wasserstein distances.- 3. Estimation of transport maps.- 4. Entropic optimal transport.- 5. Wasserstein gradient flows: theory.-6. Wasserstein gradient flows: applications.- 7. Metric geometry of the Wasserstein space.- 8. Wasserstein barycenters.