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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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Главные авторы: Chewi, Sinho (Автор), Niles-Weed, Jonathan (Автор), Rigollet, Philippe (Автор)
Соавтор: SpringerLink (Online service)
Формат: Электронный ресурс eКнига
Язык:английский
Опубликовано: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Редактирование:1st ed. 2025.
Серии:École d'Été de Probabilités de Saint-Flour ; 2364
Предметы:
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-ссылка:https://doi.org/10.1007/978-3-031-85160-5
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Оглавление:
  • 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.

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