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| author | Minhui Huang <32522773+mhhuang95@users.noreply.github.com> | 2021-09-06 08:06:50 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-09-06 17:06:50 +0200 |
| commit | 96bf1a46e74d6985419e14222afb0b9241a7bb36 (patch) | |
| tree | 6d2b89760a5e3568a79df5c96bc30439c2e82297 /README.md | |
| parent | c105dcb892de87ae9c6cfcfc5d9c0b14f2933082 (diff) | |
[MRG] Projection Robust Wasserstein (#267)
* ot.dr: PRW code; text.text_dr: PRW test code.
* ot.dr: PRW code; test.test_dr: PRW test code.
* fix errors: pep8(3.8)
* fix errors: pep8(3.8)
* modified readme; prw code review
* fix pep error
* edit comment
* modified math comment
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 3 |
1 files changed, 3 insertions, 0 deletions
@@ -198,6 +198,7 @@ The contributors to this library are * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) * [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT) * [Adrien Corenflos](https://adriencorenflos.github.io/) (Sliced Wasserstein Distance) +* [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance) 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): @@ -283,3 +284,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. [31] Bonneel, Nicolas, et al. [Sliced and radon wasserstein barycenters of measures](https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf), Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45 + +[32] Huang, M., Ma S., Lai, L. (2021). [A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance](http://proceedings.mlr.press/v139/huang21e.html), Proceedings of the 38th International Conference on Machine Learning (ICML). |