Merge pull request #23 from rflamary/gromov

Gromov-Wasserstein distance

1 files changed, 3 insertions, 1 deletions

diff --git a/README.md b/README.md
index 8d54f49..e394fc3 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ It provides the following solvers:
 * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
 * Joint OT matrix and mapping estimation [8].
 * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
-
+* Gromov-Wasserstein distances and barycenters [12]
 
 Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
@@ -184,3 +184,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816.
 
 [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063.
+
+[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html)  International Conference on Machine Learning (ICML). 2016.