diff options
| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-08-29 13:20:42 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-08-29 13:20:42 +0200 |
| commit | 8babe1b307efa3539451b72f6c686fddc1d29cda (patch) | |
| tree | 975d90b4460fc336870029eca35ed562fa125473 /docs | |
| parent | a460ce86b1169de0d80ad7dc4b28abcdb9e47cb2 (diff) | |
update doc
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/source/readme.rst | 19 |
1 files changed, 19 insertions, 0 deletions
diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5d37f64..d10b769 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -19,6 +19,7 @@ It provides the following solvers: squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +- Non regularized free support Wasserstein barycenters [20]. - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -29,6 +30,8 @@ It provides the following solvers: pymanopt). - Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +- Stochastic Optimization for Large-scale Optimal Transport (semi-dual + problem [18] and dual problem [19]) Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -219,6 +222,8 @@ The contributors to this library are: - `Stanislas Chambon <https://slasnista.github.io/>`__ - `Antoine Rolet <https://arolet.github.io/>`__ - Erwan Vautier (Gromov-Wasserstein) +- `Kilian Fatras <https://kilianfatras.github.io/>`__ (Stochastic + optimization) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -334,6 +339,20 @@ Optimal Transport <https://arxiv.org/abs/1710.06276>`__. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic +Optimization for Large-scale Optimal +Transport <arXiv%20preprint%20arxiv:1605.08527>`__. Advances in Neural +Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, +A.& Blondel, M. `Large-scale Optimal Transport and Mapping +Estimation <https://arxiv.org/pdf/1711.02283.pdf>`__. International +Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein +Barycenters <http://proceedings.mlr.press/v32/cuturi14.html>`__. +International Conference in Machine Learning + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg |