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Statut |
Confirmé |
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Série |
FDLPENS |
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Domaines |
cond-mat.stat-mech,math-ph,math.MP,physics.comp-ph |
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Date |
Mardi 23 Avril 2024 |
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Heure |
10:00 |
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Institut |
LPENS |
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Salle |
Salle Langevin, 29 RUE D'ULM |
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Nom de l'orateur |
PeyrÉ |
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Prenom de l'orateur |
Gabriel |
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Addresse email de l'orateur |
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Institution de l'orateur |
DMA-ENS |
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Titre |
An Introduction to Computational Optimal Transport (Tutorial) |
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Résumé |
Optimal transport (OT) is a fundamental mathematical theory at the interface between optimization, partial
differential equations and probability. It has recently emerged as an important tool to tackle a surprisingly large
range of problems in data sciences, such as shape registration in medical imaging, structured prediction problems
in supervised learning, and training deep generative networks. This mini-course will interleave the description of
the mathematical theory with the recent developments of scalable numerical solvers. This will highlight the
importance of recent advances in regularized approaches for OT which allow one to tackle high dimensional
learning problems. Material for the course (including a small book, slides, and computational resources) can be
found online at https://optimaltransport.github.io/. |
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Numéro de preprint arXiv |
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Commentaires |
*** 2h30 Tutorial with 30' break in the middle ***
(1st of 3 talks in the Focus Day on Optimal Transport) |
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Fichiers attachés |
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