POT: Python Optimal Transport

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. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

Authors

• Rémi Flamary CMAP, Ecole Polytechnique, IP Paris
• Nicolas Courty Université Bretagne Sud, IRISA
• Alexandre Gramfort
• Mokhtar Z. Alaya
• Aurélie Boisbunon
• Stanislas Chambon
• Laetitia Chapel
• Adrien Corenflos
• Kilian Fatras
• Nemo Fournier
• Léo Gautheron
• Nathalie T.H. Gayraud
• Hicham Janati LTCI, Télécom Paris, IP Paris
• Alain Rakotomamonjy
• Ievgen Redko Univ. Lyon, UJM-Saint-Etienne, CNRS, UMR 5516
• Antoine Rolet
• Antony Schutz
• Vivien Seguy
• Danica J. Sutherland
• Romain Tavenard
• Alexander Tong
• Titouan Vayer

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