POT: Python Optimal Transport

Remi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z Alaya, Aurelie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Leo Gautheron, Nathalie T H Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J Sutherland, Romain Tavenard, Alex Tong, Titouan Vayer
April, 2021 archival
PDF Cite Code

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.

Type
Journal article
Publication
In Journal of Machine Learning Research
Alex Tong
Alex Tong
Postdoctoral Fellow

My research interests include optimal transport, graph scattering, and normalizing flows.