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| author | Rémi Flamary <remi.flamary@gmail.com> | 2020-04-15 13:16:30 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-04-15 13:16:30 +0200 |
| commit | adc5570550676b63b9aabb2205a67c5b7c9187f3 (patch) | |
| tree | 0082b2ea3843bb50738eb4689fb1eb9c74b85034 /README.md | |
| parent | 4cd4e09f89fe6f95a07d632365612b797ab760da (diff) | |
| parent | 7889484b79a425ebf3632444547a6092e814bf20 (diff) | |
Merge pull request #137 from ievred/jcpot
[MRG] Jcpot : Multi source DA with target shift
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 3 |
1 files changed, 3 insertions, 0 deletions
@@ -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 domain adaptation with 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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