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@@ -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).
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+* Gromov-Wasserstein distances [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.
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+[12] Peyré, Gabriel, 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.