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Statistical Optimal Transport École d'Été de Probabilités de Saint-Flour XLIX – 2019 /

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...

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Bibliographic Details
Main Authors: Chewi, Sinho (Author), Niles-Weed, Jonathan (Author), Rigollet, Philippe (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edition:1st ed. 2025.
Series:École d'Été de Probabilités de Saint-Flour ; 2364
Subjects:
Statistics .
Machine learning.
Mathematical optimization.
Calculus of variations.
Statistical Physics.
Probabilities.
Statistical Theory and Methods.
Machine Learning.
Calculus of Variations and Optimization.
Probability Theory.
Online Access:https://doi.org/10.1007/978-3-031-85160-5
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Summary: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.
Physical Description:1 online resource (XIV, 260 p. 10 illus.). 3 illus. in color.
ISBN:9783031851605
DOI:10.1007/978-3-031-85160-5

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