POT: Python Optimal Transport
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.
Cite
Text
Flamary et al. "POT: Python Optimal Transport." Machine Learning Open Source Software, 2021.Markdown
[Flamary et al. "POT: Python Optimal Transport." Machine Learning Open Source Software, 2021.](https://mlanthology.org/mloss/2021/flamary2021jmlr-pot/)BibTeX
@article{flamary2021jmlr-pot,
title = {{POT: Python Optimal Transport}},
author = {Flamary, Rémi and Courty, Nicolas and Gramfort, Alexandre and Alaya, Mokhtar Z. and Boisbunon, Aurélie and Chambon, Stanislas and Chapel, Laetitia and Corenflos, Adrien and Fatras, Kilian and Fournier, Nemo and Gautheron, Léo and Gayraud, Nathalie T.H. and Janati, Hicham and Rakotomamonjy, Alain and Redko, Ievgen and Rolet, Antoine and Schutz, Antony and Seguy, Vivien and Sutherland, Danica J. and Tavenard, Romain and Tong, Alexander and Vayer, Titouan},
journal = {Machine Learning Open Source Software},
year = {2021},
pages = {1-8},
volume = {22},
url = {https://mlanthology.org/mloss/2021/flamary2021jmlr-pot/}
}