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GeezerVison is showing pythonot.github.io — Contributors — POT Python Optimal Transport 0.9.7 documentation

Contributors

Creators and Maintainers

This toolbox has been created by Rémi Flamary and Nicolas Courty.

It is currently maintained by :

  • Rémi Flamary

  • Cédric Vincent-Cuaz

Contributors

The contributors to this library are:

  • Rémi Flamary (EMD wrapper, Pytorch backend, DA classes, conditional gradients, WDA, weak OT, linear OT mapping, documentation)

  • Nicolas Courty (Original sinkhorn, Wasserstein barycenters and convolutional barycenters, 1D wasserstein)

  • Alexandre Gramfort (CI, documentation)

  • Laetitia Chapel (Partial OT, Unbalanced OT non-regularized)

  • Michael Perrot (Mapping estimation)

  • Léo Gautheron (Initial GPU implementation)

  • Nathalie Gayraud (DA classes)

  • Stanislas Chambon (DA classes)

  • Antoine Rolet (EMD solver debug)

  • Erwan Vautier (Gromov-Wasserstein)

  • Kilian Fatras (Stochastic solvers, empirical sinkhorn)

  • Alain Rakotomamonjy (Greenkhorn)

  • Vayer Titouan (Gromov-Wasserstein, Fused-Gromov-Wasserstein)

  • Hicham Janati (Unbalanced OT, Debiased barycenters)

  • Romain Tavenard (1D Wasserstein)

  • Mokhtar Z. Alaya (Screenkhorn)

  • Ievgen Redko (Laplacian DA, JCPOT)

  • Adrien Corenflos (Sliced Wasserstein Distance)

  • Tanguy Kerdoncuff (Sampled Gromov Wasserstein)

  • Minhui Huang (Projection Robust Wasserstein Distance)

  • Nathan Cassereau (Backends)

  • Cédric Vincent-Cuaz (Graph Dictionary Learning, FGW, semi-relaxed FGW, quantized FGW, partial FGW)

  • Eloi Tanguy (Generalized Wasserstein Barycenters, GMMOT, Barycenters for General Transport Costs)

  • Camille Le Coz (EMD2 debug)

  • Eduardo Fernandes Montesuma (Free support sinkhorn barycenter)

  • Theo Gnassounou (OT between Gaussian distributions)

  • Clément Bonet (Wasserstein on circle, Spherical Sliced-Wasserstein)

  • Ronak Mehta (Efficient Discrete Multi Marginal Optimal Transport Regularization)

  • Xizheng Yu (Efficient Discrete Multi Marginal Optimal Transport Regularization)

  • Sonia Mazelet (Template based GNN layers)

  • Laurène David (Low rank sinkhorn, Low rank Gromov-Wasserstein samples)

  • Julie Delon (GMM OT)

  • Samuel Boïté (GMM OT)

  • Nathan Neike (Sparse EMD solver)

  • Thibaut Germain (SGOT)

  • Sienna O’Shea (SGOT)

Acknowledgments

This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):

  • Gabriel Peyré (Wasserstein Barycenters in Matlab)

  • Mathieu Blondel (original implementation smooth OT)

  • Nicolas Bonneel (C++ code for EMD)

  • Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)

POT has benefited from the financing or manpower from the following partners:

ANRCNRS3IAHi!PARIS

Python Optimal Transport 0.9.7
Versions: Release Development Code
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