From cec41d3817067a2eb3031092735347efe4184237 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 5 Nov 2021 17:13:14 +0100
Subject: [MRG] Release 0.8 (#289)

* working on release

* test circleci

* try again

* cleanup circle ci run

* add all PR and releant Issues

* update doc

* thanks idris

* update version + add pyproject.toml

* test pyproject.toml

* revert tests

* build wheels

* use windows-latest for tests

* add tests python 3.10

* build all whels

* all versions

* build all wheels

* build all wheels

* cleanup pep8 and minimal acions

* forst shot text release

* bettr text

* stuff

* release text updated

* update manifest to allow build from source

* update doc again

* update release
---
 docs/source/readme.rst | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

(limited to 'docs/source/readme.rst')

diff --git a/docs/source/readme.rst b/docs/source/readme.rst
index ee32e2b..a8f1bc0 100644
--- a/docs/source/readme.rst
+++ b/docs/source/readme.rst
@@ -34,6 +34,9 @@ POT provides the following generic OT solvers (links to examples):
    [21] and unmixing [4].
 -  Sinkhorn divergence [23] and entropic regularization OT from
    empirical data.
+-  Debiased Sinkhorn barycenters `Sinkhorn divergence
+   barycenter <auto_examples/barycenters/plot_debiased_barycenter.html>`__
+   [37]
 -  `Smooth optimal transport
    solvers <auto_examples/plot_OT_1D_smooth.html>`__
    (dual and semi-dual) for KL and squared L2 regularizations [17].
@@ -44,7 +47,8 @@ POT provides the following generic OT solvers (links to examples):
    distances <auto_examples/gromov/plot_gromov.html>`__
    and `GW
    barycenters <auto_examples/gromov/plot_gromov_barycenter.html>`__
-   (exact [13] and regularized [12])
+   (exact [13] and regularized [12]), differentiable using gradients
+   from
 -  `Fused-Gromov-Wasserstein distances
    solver <auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__
    and `FGW
@@ -70,7 +74,8 @@ POT provides the following generic OT solvers (links to examples):
    (exact [29] and entropic [3] formulations).
 -  `Sliced
    Wasserstein <auto_examples/sliced-wasserstein/plot_variance.html>`__
-   [31, 32].
+   [31, 32] and Max-sliced Wasserstein [35] that can be used for
+   gradient flows [36].
 -  `Several
    backends <https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends>`__
    for easy use of POT with
@@ -278,7 +283,8 @@ The contributors to this library are
    Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__
 -  `Vayer Titouan <https://tvayer.github.io/>`__ (Gromov-Wasserstein -,
    Fused-Gromov-Wasserstein)
--  `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT)
+-  `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT,
+   Debiased barycenters)
 -  `Romain Tavenard <https://rtavenar.github.io/>`__ (1d Wasserstein)
 -  `Mokhtar Z. Alaya <http://mzalaya.github.io/>`__ (Screenkhorn)
 -  `Ievgen Redko <https://ievred.github.io/>`__ (Laplacian DA, JCPOT)
@@ -501,6 +507,22 @@ gans <https://openaccess.thecvf.com/content_CVPR_2019/papers/Deshpande_Max-Slice
 In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
 Recognition (pp. 10648-10656).
 
+[36] Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., & Stöter, F.
+R. (2019, May). `Sliced-Wasserstein flows: Nonparametric generative
+modeling via optimal transport and
+diffusions <http://proceedings.mlr.press/v97/liutkus19a/liutkus19a.pdf>`__.
+In International Conference on Machine Learning (pp. 4104-4113). PMLR.
+
+[37] Janati, H., Cuturi, M., Gramfort, A. `Debiased sinkhorn
+barycenters <http://proceedings.mlr.press/v119/janati20a/janati20a.pdf>`__
+Proceedings of the 37th International Conference on Machine Learning,
+PMLR 119:4692-4701, 2020
+
+[38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty,
+`Online Graph Dictionary
+Learning <https://arxiv.org/pdf/2102.06555.pdf>`__, International
+Conference on Machine Learning (ICML), 2021.
+
 .. |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
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