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@@ -29,6 +29,7 @@ It provides the following solvers:
 * Non regularized free support Wasserstein barycenters [20].
 * Unbalanced OT with KL relaxation distance and barycenter [10, 25].
 * Screening Sinkhorn Algorithm for OT [26].
+* JCPOT algorithm for multi-source target shift [27].
 
 Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
@@ -257,3 +258,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/)  Advances in Neural Information Processing Systems (NIPS).
 
 [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.
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