Minor typos in the documentations (#756)

* Fixed last author's name

* Fixed minor typos in the User Guide

Author: tatsuookubo

README.md

@@ -383,7 +383,7 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer
 
 [50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR).
 
-[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
+[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
 
 [52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). [Entropic Wasserstein Component Analysis](https://arxiv.org/abs/2303.05119). ArXiv.
 

docs/source/user_guide.rst

@@ -380,10 +380,10 @@ More details about the algorithms used are given in the following note.
     In addition to all those variants of Sinkhorn, we have another
     implementation solving the problem in the smooth dual or semi-dual in
     :any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize`
-    function to solve the smooth problem with :code:`L-BFGS-B` algorithm. Tu use
+    function to solve the smooth problem with :code:`L-BFGS-B` algorithm. To use
     this solver, use functions :any:`ot.smooth.smooth_ot_dual` or
     :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to
-    choose entropic/Kullbach-Leibler regularization.
+    choose entropic/Kullback-Leibler regularization.
 
     **Choosing a Sinkhorn solver**
 

examples/gromov/plot_fgw_solvers.py

@@ -24,7 +24,7 @@
 "Optimal Transport for structured data with application on graphs"
 International Conference on Machine Learning (ICML). 2019.
 
-[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).
+[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019).
 "Gromov-wasserstein learning for graph matching and node embedding".
 In International Conference on Machine Learning (ICML), 2019.
 

examples/gromov/plot_gromov.py

@@ -26,7 +26,7 @@
 [33] Kerdoncuff T., Emonet R., Marc S. "Sampled Gromov Wasserstein",
 Machine Learning Journal (MJL), 2021.
 
-[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).
+[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019).
 "Gromov-wasserstein learning for graph matching and node embedding".
 In International Conference on Machine Learning (ICML), 2019.
 

ot/gromov/_bregman.py

@@ -142,7 +142,7 @@ def entropic_gromov_wasserstein(
         distance between networks and stable network invariants.
         Information and Inference: A Journal of the IMA, 8(4), 757-787.
 
-    .. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
+    .. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
         learning for graph matching and node embedding. In International
         Conference on Machine Learning (ICML), 2019.
     """
@@ -369,7 +369,7 @@ def entropic_gromov_wasserstein2(
         "Gromov-Wasserstein averaging of kernel and distance matrices."
         International Conference on Machine Learning (ICML). 2016.
 
-    .. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
+    .. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
         learning for graph matching and node embedding. In International
         Conference on Machine Learning (ICML), 2019.
     """
@@ -1125,7 +1125,7 @@ def entropic_fused_gromov_wasserstein(
         distance between networks and stable network invariants.
         Information and Inference: A Journal of the IMA, 8(4), 757-787.
 
-    .. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
+    .. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
         learning for graph matching and node embedding. In International
         Conference on Machine Learning (ICML), 2019.
 
@@ -1358,7 +1358,7 @@ def entropic_fused_gromov_wasserstein2(
         "Gromov-Wasserstein averaging of kernel and distance matrices."
         International Conference on Machine Learning (ICML). 2016.
 
-    .. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
+    .. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
         learning for graph matching and node embedding. In International
         Conference on Machine Learning (ICML), 2019.