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@@ -35,7 +35,7 @@ POT provides the following generic OT solvers (links to examples): Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33] * Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20]. -* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. +* [One dimensional Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. Also [exact unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html) with KL and quadratic regularization and the [regularization path of UOT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html) [41] * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36]. @@ -309,4 +309,6 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer [39] Gozlan, N., Roberto, C., Samson, P. M., & Tetali, P. (2017). [Kantorovich duality for general transport costs and applications](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.1825&rep=rep1&type=pdf). Journal of Functional Analysis, 273(11), 3327-3405. -[40] Forrow, A., Hütter, J. C., Nitzan, M., Rigollet, P., Schiebinger, G., & Weed, J. (2019, April). [Statistical optimal transport via factored couplings](http://proceedings.mlr.press/v89/forrow19a/forrow19a.pdf). In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2454-2465). PMLR.
\ No newline at end of file +[40] Forrow, A., Hütter, J. C., Nitzan, M., Rigollet, P., Schiebinger, G., & Weed, J. (2019, April). [Statistical optimal transport via factored couplings](http://proceedings.mlr.press/v89/forrow19a/forrow19a.pdf). In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2454-2465). PMLR. + +[41] Chapel*, L., Flamary*, R., Wu, H., Févotte, C., Gasso, G. (2021). [Unbalanced Optimal Transport through Non-negative Penalized Linear Regression](https://proceedings.neurips.cc/paper/2021/file/c3c617a9b80b3ae1ebd868b0017cc349-Paper.pdf) Advances in Neural Information Processing Systems (NeurIPS), 2020. (Two first co-authors)
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