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Journal Articles Journal of Machine Learning Research Year : 2021

POT : Python Optimal Transport

Aurélie Boisbunon
  • Function : Author
  • PersonId : 910949
Stanislas Chambon
Adrien Corenflos
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  • PersonId : 1102455
Nemo Fournier
Léo Gautheron
Nathalie T H Gayraud
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  • PersonId : 1102414
Ievgen Redko
Antoine Rolet
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  • PersonId : 1102416
Antony Schutz
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  • PersonId : 1102417
Vivien Seguy
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  • PersonId : 1102418
Romain Tavenard
Alexander Tong
Titouan Vayer

Abstract

Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel efficient optimization procedures allowing for medium to large scale applications. We propose a Python toolbox that implements several key optimal transport ideas for the machine learning community. The toolbox contains implementations of a number of founding works of OT for machine learning such as Sinkhorn algorithm and Wasserstein barycenters, but also provides generic solvers that can be used for conducting novel fundamental research. This toolbox, named POT for Python Optimal Transport, is open source with an MIT license.
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Dates and versions

hal-03264013 , version 1 (17-06-2021)

Identifiers

  • HAL Id : hal-03264013 , version 1

Cite

Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Zahdi Alaya, Aurélie Boisbunon, et al.. POT : Python Optimal Transport. Journal of Machine Learning Research, 2021. ⟨hal-03264013⟩
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