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Title: GitHub - PythonOT/POT: POT : Python Optimal Transport

Open Graph Title: GitHub - PythonOT/POT: POT : Python Optimal Transport

X Title: GitHub - PythonOT/POT: POT : Python Optimal Transport

Description: POT : Python Optimal Transport. Contribute to PythonOT/POT development by creating an account on GitHub.

Open Graph Description: POT : Python Optimal Transport. Contribute to PythonOT/POT development by creating an account on GitHub.

X Description: POT : Python Optimal Transport. Contribute to PythonOT/POT development by creating an account on GitHub.

Opengraph URL: https://github.com/PythonOT/POT

X: @github

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OT Network Simplex solverhttps://pythonot.github.io/auto_examples/plot_OT_1D.html
Conditional gradienthttps://pythonot.github.io/auto_examples/plot_optim_OTreg.html
Generalized conditional gradienthttps://pythonot.github.io/auto_examples/plot_optim_OTreg.html
Sinkhorn Knopp Algorithmhttps://pythonot.github.io/auto_examples/plot_OT_1D.html
Wasserstein barycenterhttps://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html
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Sinkhorn divergence barycenterhttps://pythonot.github.io/auto_examples/barycenters/plot_debiased_barycenter.html
Wasserstein barycenters [16] https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html
Gromov-Wasserstein distanceshttps://pythonot.github.io/auto_examples/gromov/plot_gromov.html
GW barycentershttps://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html
Fused-Gromov-Wasserstein distances solverhttps://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py
FGW barycentershttps://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html
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differentiable losseshttps://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html
Sampled solver of Gromov Wassersteinhttps://pythonot.github.io/auto_examples/gromov/plot_gromov.html
free support Wasserstein barycentershttps://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html
One dimensional Unbalanced OThttps://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html
barycenterhttps://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html
exact unbalanced OThttps://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html
regularization path of UOThttps://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html
Partial Wasserstein and Gromov-Wassersteinhttps://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html
Partial Fused Gromov-Wassersteinhttps://pythonot.github.io/auto_examples/gromov/plot_partial_fgw.html
Sliced Wassersteinhttps://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html
Wasserstein distance on the circlehttps://pythonot.github.io/auto_examples/sliced-wasserstein/plot_compute_wasserstein_circle.html
Spherical Sliced Wassersteinhttps://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html
Graph Dictionary Learning solvershttps://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html
Semi-relaxed (Fused) Gromov-Wasserstein divergenceshttps://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html
barycenter solvershttps://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.hmtl
Quantized (Fused) Gromov-Wasserstein distanceshttps://pythonot.github.io/auto_examples/gromov/plot_quantized_gromov_wasserstein.html
Efficient Discrete Multi Marginal Optimal Transport Regularizationhttps://pythonot.github.io/auto_examples/others/plot_demd_gradient_minimize.html
Several backendshttps://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends
Pytorchhttps://pytorch.org/
jaxhttps://github.com/google/jax
Numpyhttps://numpy.org/
Cupyhttps://cupy.dev/
Tensorflowhttps://www.tensorflow.org/
Smooth Strongly Convex Nearest Brenier Potentialshttps://pythonot.github.io/auto_examples/others/plot_SSNB.html#sphx-glr-auto-examples-others-plot-ssnb-py
Gaussian Mixture Model OThttps://pythonot.github.io/auto_examples/gaussian_gmm/plot_GMMOT_plan.html#sphx-glr-auto-examples-others-plot-gmmot-plan-py
Co-Optimal Transporthttps://pythonot.github.io/auto_examples/others/plot_COOT.html
unbalanced Co-Optimal Transporthttps://pythonot.github.io/auto_examples/others/plot_learning_weights_with_COOT.html
Optimal Transport Barycenters for Generic Costshttps://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter_generic_cost.html
Barycenters between Gaussian Mixture Modelshttps://pythonot.github.io/auto_examples/barycenters/plot_gmm_barycenter.html
Optimal transport for domain adaptationhttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html
group lasso regularizationhttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html
Laplacian regularizationhttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html
semi supervised settinghttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html
Linear OT mappinghttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html
Joint OT mapping estimationhttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html
Wasserstein Discriminant Analysishttps://pythonot.github.io/auto_examples/others/plot_WDA.html
JCPOT algorithm for multi-source domain adaptation with target shifthttps://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html
Graph Neural Network OT layers TFGWhttps://pythonot.github.io/auto_examples/gromov/plot_gnn_TFGW.html
documentationhttps://pythonot.github.io/auto_examples/index.html
https://patch-diff.githubusercontent.com/PythonOT/POT#using-and-citing-the-toolbox
JMLR paperhttps://jmlr.org/papers/v22/20-451.html
https://patch-diff.githubusercontent.com/PythonOT/POT#installation
https://patch-diff.githubusercontent.com/PythonOT/POT#pip-installation
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https://PythonOT.github.io/https://PythonOT.github.io/
https://patch-diff.githubusercontent.com/PythonOT/POT#acknowledgements
Rémi Flamaryhttps://remi.flamary.com/
Nicolas Courtyhttp://people.irisa.fr/Nicolas.Courty/
Rémi Flamaryhttps://remi.flamary.com/
Cédric Vincent-Cuazhttps://cedricvincentcuaz.github.io/
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https://patch-diff.githubusercontent.com/PythonOT/POT#references
Displacement interpolation using Lagrangian mass transporthttps://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf
Sinkhorn distances: Lightspeed computation of optimal transporthttps://arxiv.org/pdf/1306.0895.pdf
Iterative Bregman projections for regularized transportation problemshttps://arxiv.org/pdf/1412.5154.pdf
Supervised planetary unmixing with optimal transporthttps://hal.archives-ouvertes.fr/hal-01377236/document
Optimal Transport for Domain Adaptationhttps://arxiv.org/pdf/1507.00504.pdf
Regularized discrete optimal transporthttps://arxiv.org/pdf/1307.5551.pdf
Generalized conditional gradient: analysis of convergence and applicationshttps://arxiv.org/pdf/1510.06567.pdf
Mapping estimation for discrete optimal transporthttp://remi.flamary.com/biblio/perrot2016mapping.pdf
Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problemshttps://arxiv.org/pdf/1610.06519.pdf
Scaling algorithms for unbalanced transport problemshttps://arxiv.org/pdf/1607.05816.pdf
Wasserstein Discriminant Analysishttps://arxiv.org/pdf/1608.08063.pdf
Gromov-Wasserstein averaging of kernel and distance matriceshttp://proceedings.mlr.press/v48/peyre16.html
Gromov–Wasserstein distances and the metric approach to object matchinghttps://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf
On the optimal mapping of distributionshttps://link.springer.com/article/10.1007/BF00934745
Computational Optimal Transporthttps://arxiv.org/pdf/1803.00567.pdf
Barycenters in the Wasserstein spacehttps://hal.archives-ouvertes.fr/hal-00637399/document
Smooth and Sparse Optimal Transporthttps://arxiv.org/abs/1710.06276
Stochastic Optimization for Large-scale Optimal Transporthttps://arxiv.org/abs/1605.08527
Large-scale Optimal Transport and Mapping Estimationhttps://arxiv.org/pdf/1711.02283.pdf
Fast Computation of Wasserstein Barycentershttp://proceedings.mlr.press/v32/cuturi14.html
Convolutional wasserstein distances: Efficient optimal transportation on geometric domainshttps://dl.acm.org/citation.cfm?id=2766963
Near-linear time approximation algorithms for optimal transport via Sinkhorn iterationhttps://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf
Learning Generative Models with Sinkhorn Divergenceshttps://arxiv.org/abs/1706.00292
Optimal Transport for structured data with application on graphshttp://proceedings.mlr.press/v97/titouan19a.html
Learning with a Wasserstein Losshttp://cbcl.mit.edu/wasserstein/
Screening Sinkhorn Algorithm for Regularized Optimal Transporthttps://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport
Optimal Transport for Multi-source Domain Adaptation under Target Shifthttp://proceedings.mlr.press/v89/redko19a.html
Free boundaries in optimal transport and Monge-Ampere obstacle problemshttp://www.math.toronto.edu/~mccann/papers/annals2010.pdf
Partial Optimal Transport with Applications on Positive-Unlabeled Learninghttps://arxiv.org/abs/2002.08276
Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matchinghttps://remi.flamary.com/biblio/flamary2014optlaplace.pdf
Sliced and radon wasserstein barycenters of measureshttps://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distancehttp://proceedings.mlr.press/v139/huang21e.html
Sampled Gromov Wassersteinhttps://hal.archives-ouvertes.fr/hal-03232509/document
Interpolating between optimal transport and MMD using Sinkhorn divergenceshttp://proceedings.mlr.press/v89/feydy19a/feydy19a.pdf
Max-sliced wasserstein distance and its use for ganshttps://openaccess.thecvf.com/content_CVPR_2019/papers/Deshpande_Max-Sliced_Wasserstein_Distance_and_Its_Use_for_GANs_CVPR_2019_paper.pdf
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusionshttp://proceedings.mlr.press/v97/liutkus19a/liutkus19a.pdf
Debiased sinkhorn barycentershttp://proceedings.mlr.press/v119/janati20a/janati20a.pdf
Online Graph Dictionary Learninghttps://arxiv.org/pdf/2102.06555.pdf
Kantorovich duality for general transport costs and applicationshttps://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.1825&rep=rep1&type=pdf
Statistical optimal transport via factored couplingshttp://proceedings.mlr.press/v89/forrow19a/forrow19a.pdf
Unbalanced Optimal Transport through Non-negative Penalized Linear Regressionhttps://proceedings.neurips.cc/paper/2021/file/c3c617a9b80b3ae1ebd868b0017cc349-Paper.pdf
Generalized Wasserstein barycenters between probability measures living on different subspaceshttps://arxiv.org/pdf/2105.09755
A fixed-point approach to barycenters in Wasserstein space.https://arxiv.org/pdf/1511.05355.pdf
Fast transport optimization for Monge costs on the circle.https://arxiv.org/abs/0902.3527
The statistics of circular optimal transport.https://arxiv.org/abs/2103.15426
Spherical Sliced-Wassersteinhttps://openreview.net/forum?id=jXQ0ipgMdU
The gromov–wasserstein distance between networks and stable network invariantshttps://academic.oup.com/imaiai/article/8/4/757/5627736
Semi-relaxed Gromov-Wasserstein divergence and applications on graphshttps://openreview.net/pdf?id=RShaMexjc-x
CO-Optimal Transporthttps://proceedings.neurips.cc/paper/2020/file/cc384c68ad503482fb24e6d1e3b512ae-Paper.pdf
Sparsity-constrained optimal transporthttps://openreview.net/forum?id=yHY9NbQJ5BP
Gromov-wasserstein learning for graph matching and node embeddinghttp://proceedings.mlr.press/v97/xu19b.html
Entropic Wasserstein Component Analysishttps://arxiv.org/abs/2303.05119
Template based graph neural network with optimal transport distanceshttps://papers.nips.cc/paper_files/paper/2022/file/4d3525bc60ba1adc72336c0392d3d902-Paper-Conference.pdf
Optimal transport graph neural networkshttps://arxiv.org/pdf/2006.04804
Efficient Discrete Multi Marginal Optimal Transport Regularizationhttps://openreview.net/forum?id=R98ZfMt-jE
Properties of the d-dimensional earth mover’s problemhttps://www.sciencedirect.com/science/article/pii/S0166218X19301441
Gromov–Wasserstein distances between Gaussian distributionshttps://hal.science/hal-03197398v2/file/main.pdf
Regularity as regularization:Smooth and strongly convex brenier potentials in optimal transport.http://proceedings.mlr.press/v108/paty20a/paty20a.pdf
Convex interpolation and performance estimation of first-order methods for convex optimization.https://dial.uclouvain.be/pr/boreal/object/boreal%3A182881/datastream/PDF_01/view
Fast and scalable optimal transport for brain tractogramshttps://arxiv.org/pdf/2107.02010.pdf
Kernel operations on the gpu, with autodiff, without memory overflowshttps://www.jmlr.org/papers/volume22/20-275/20-275.pdf
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wassersteinhttps://arxiv.org/pdf/2310.03398.pdf
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Datahttps://openreview.net/pdf?id=0jxPyVWmiiF
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classificationshttps://openreview.net/pdf?id=uqkUguNu40
Low-Rank Sinkhorn Factorizationhttps://arxiv.org/pdf/2103.04737.pdf
Entropic estimation of optimal transport mapshttps://arxiv.org/pdf/2109.12004.pdf
Linear-Time Gromov-Wasserstein Distances using Low Rank Couplings and Costshttps://proceedings.mlr.press/v162/scetbon22b/scetbon22b.pdf
Quantized gromov-wassersteinhttps://link.springer.com/chapter/10.1007/978-3-030-86523-8_49
A Wasserstein-type distance in the space of Gaussian mixture modelshttps://epubs.siam.org/doi/abs/10.1137/19M1301047
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein.https://proceedings.neurips.cc/paper_files/paper/2022/file/8906cac4ca58dcaf17e97a0486ad57ca-Paper-Conference.pdf
Unbalanced Co-Optimal Transporthttps://dl.acm.org/doi/10.1609/aaai.v37i8.26193
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxationhttps://proceedings.neurips.cc/paper/2021/file/4990974d150d0de5e6e15a1454fe6b0f-Paper.pdf
Faster Unbalanced Optimal Transport: Translation Invariant Sinkhorn and 1-D Frank-Wolfehttps://proceedings.mlr.press/v151/sejourne22a.html
Gradient descent algorithms for Bures-Wasserstein barycentershttps://proceedings.mlr.press/v125/chewi20a.html
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descenthttps://papers.neurips.cc/paper_files/paper/2021/hash/b9acb4ae6121c941324b2b1d3fac5c30-Abstract.html
One for all and all for one: Efficient computation of partial Wasserstein distances on the linehttps://iclr.cc/virtual/2025/poster/28547
Computing Barycentres of Measures for Generic Transport Costshttps://arxiv.org/abs/2501.04016
LCOT: Linear Circular Optimal Transporthttps://openreview.net/forum?id=49z97Y9lMq
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Datahttps://openreview.net/forum?id=fgUFZAxywx
Massively scalable Sinkhorn distances via the Nyström methodhttps://proceedings.neurips.cc/paper_files/paper/2019/file/f55cadb97eaff2ba1980e001b0bd9842-Paper.pdf
Scalable Gromov-Wasserstein learning for graph partitioning and matchinghttps://proceedings.neurips.cc/paper/2019/hash/6e62a992c676f611616097dbea8ea030-Abstract.html
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