diff --git a/lib/scholar/manifold/tsne.ex b/lib/scholar/manifold/tsne.ex
index 50245689..626ca990 100644
--- a/lib/scholar/manifold/tsne.ex
+++ b/lib/scholar/manifold/tsne.ex
@@ -1,6 +1,10 @@
 defmodule Scholar.Manifold.TSNE do
   @moduledoc """
-  TSNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction technique.
+  t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction technique.
+
+  The time complexity is $O(N^2)$ for $N$ samples. There are known $O(N*\\log(N))$ implementations but
+  those use tree structures which are not available in Nx (as Nx algorithms are also designed to run on
+  the GPU).
 
   ## Reference
 
diff --git a/lib/scholar/neighbors/k_nearest_neighbors.ex b/lib/scholar/neighbors/k_nearest_neighbors.ex
index 0223a3b6..6528e144 100644
--- a/lib/scholar/neighbors/k_nearest_neighbors.ex
+++ b/lib/scholar/neighbors/k_nearest_neighbors.ex
@@ -1,6 +1,9 @@
 defmodule Scholar.Neighbors.KNearestNeighbors do
   @moduledoc """
-  The K-Nearest Neighbors. It implements both classification and regression.
+  The K-Nearest Neighbors.
+
+  It implements both classification and regression. The time complexity is
+  $O(N^2)$ for $N$ samples.
   """
   import Nx.Defn
   import Scholar.Shared
@@ -162,7 +165,7 @@ defmodule Scholar.Neighbors.KNearestNeighbors do
 
   ## Return Values
 
-    It returns a tensor with predicted class labels
+  It returns a tensor with predicted class labels.
 
   ## Examples
 
@@ -210,7 +213,7 @@ defmodule Scholar.Neighbors.KNearestNeighbors do
 
   ## Return Values
 
-    It returns a tensor with probabilities of classes. They are arranged in lexicographic order.
+  It returns a tensor with probabilities of classes. They are arranged in lexicographic order.
 
   ## Examples