diff --git a/README.md b/README.md
index d7e159509..3a14474d2 100644
--- a/README.md
+++ b/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.
 
diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst
index 69d48528b..b8f79ceba 100644
--- a/docs/source/user_guide.rst
+++ b/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**
 
diff --git a/examples/gromov/plot_fgw_solvers.py b/examples/gromov/plot_fgw_solvers.py
index d727e09d6..84f13acf4 100644
--- a/examples/gromov/plot_fgw_solvers.py
+++ b/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.
 
diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py
index c2ac3e43f..fbf0b7316 100644
--- a/examples/gromov/plot_gromov.py
+++ b/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.
 
diff --git a/ot/gromov/_bregman.py b/ot/gromov/_bregman.py
index fbc8d4897..597ac1524 100644
--- a/ot/gromov/_bregman.py
+++ b/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.