diff --git a/lib/scholar/manifold/tsne.ex b/lib/scholar/manifold/tsne.ex
index 626ca990..fbebf650 100644
--- a/lib/scholar/manifold/tsne.ex
+++ b/lib/scholar/manifold/tsne.ex
@@ -2,9 +2,7 @@ defmodule Scholar.Manifold.TSNE do
   @moduledoc """
   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).
+  This is an exact implementation of t-SNE and therefore it has time complexity is $O(N^2)$ for $N$ samples.
 
   ## Reference
 
diff --git a/lib/scholar/neighbors/k_nearest_neighbors.ex b/lib/scholar/neighbors/k_nearest_neighbors.ex
index 6528e144..a41ee5c8 100644
--- a/lib/scholar/neighbors/k_nearest_neighbors.ex
+++ b/lib/scholar/neighbors/k_nearest_neighbors.ex
@@ -2,8 +2,8 @@ defmodule Scholar.Neighbors.KNearestNeighbors do
   @moduledoc """
   The K-Nearest Neighbors.
 
-  It implements both classification and regression. The time complexity is
-  $O(N^2)$ for $N$ samples.
+  It implements both classification and regression. This implements the linear
+  version of kNN and therefore it has time complexity $O(N^2)$ for $N$ samples.
   """
   import Nx.Defn
   import Scholar.Shared