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authorRémi Flamary <remi.flamary@gmail.com>2019-06-25 14:33:46 +0200
committerGitHub <noreply@github.com>2019-06-25 14:33:46 +0200
commit2364d56aad650d501753cc93a69ea1b8ddf28b0a (patch)
treea92d17f75aa2e595656ab5bb58aeb3c9af16df3e /README.md
parent5a6b226de20624b51c2ff98bc30e5611a7a788c7 (diff)
parentc9df24649d359b21280328f6bd580eb049cae3d3 (diff)
Merge pull request #87 from hichamjanati/unbalanced-ot
[MRG] Add Unbalanced KL Wasserstein distance + barycenter
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@@ -27,6 +27,7 @@ It provides the following solvers:
* Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
* Non regularized free support Wasserstein barycenters [20].
+* Unbalanced OT with KL relaxation distance and barycenter [10, 25].
Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
@@ -165,6 +166,7 @@ The contributors to this library are:
* [Kilian Fatras](https://kilianfatras.github.io/)
* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home)
* [Vayer Titouan](https://tvayer.github.io/)
+* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT)
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):
@@ -236,3 +238,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018
[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML).
+
+[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS).