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@@ -15,7 +15,8 @@ It provides the following solvers:
* OT Network Flow solver for the linear program/ Earth Movers Distance [1].
* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat).
-* Non regularized Wasserstein barycenters [16] with LP solver.
+* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
+* Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
* Bregman projections for Wasserstein barycenter [3] and unmixing [4].
* Optimal transport for domain adaptation with group lasso regularization [5]
* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
@@ -29,10 +30,11 @@ Some demonstrations (both in Python and Jupyter Notebook format) are available i
If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:
```
-@article{flamary2017pot,
- title={POT Python Optimal Transport library},
- author={Flamary, R{\'e}mi and Courty, Nicolas},
- year={2017}
+@misc{flamary2017pot,
+title={POT Python Optimal Transport library},
+author={Flamary, R{'e}mi and Courty, Nicolas},
+url={https://github.com/rflamary/POT},
+year={2017}
}
```
@@ -215,3 +217,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) .
[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924.
+
+[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).