diff --git a/privacy/bolton/README.md b/privacy/bolton/README.md
index a423b65..4aef36f 100644
--- a/privacy/bolton/README.md
+++ b/privacy/bolton/README.md
@@ -5,7 +5,7 @@ of methods used in the ensuring privacy in machine learning that leverages
 additional assumptions to provide a new way of approaching the privacy
 guarantees.
 
-# Bolton Description
+## Bolton Description
 
 This method uses 4 key steps to achieve privacy guarantees:
   1. Adds noise to weights after training (output perturbation).
@@ -17,7 +17,7 @@ For more details on the strong convexity requirements, see:
 Bolt-on Differential Privacy for Scalable Stochastic Gradient
 Descent-based Analytics by Xi Wu et al.
 
-# Why Bolton?
+## Why Bolton?
 
 The major difference for the Bolton method is that it injects noise post model
 convergence, rather than noising gradients or weights during training. This
@@ -28,12 +28,12 @@ The paper describes in detail the advantages and disadvantages of this approach
 and its results compared to some other methods, namely noising at each iteration
 and no noising.
 
-# Tutorials
+## Tutorials
 
 This package has a tutorial that can be found in the root tutorials directory,
 under `bolton_tutorial.py`.
 
-# Contribution
+## Contribution
 
 This package was initially contributed by Georgian Partners with the hope of
 growing the tensorflow/privacy library. There are several rich use cases for
@@ -41,7 +41,7 @@ delta-epsilon privacy in machine learning, some of which can be explored here:
 https://medium.com/apache-mxnet/epsilon-differential-privacy-for-machine-learning-using-mxnet-a4270fe3865e
 https://arxiv.org/pdf/1811.04911.pdf
 
-# Contacts
+## Contacts
 
 In addition to the maintainers of tensorflow/privacy listed in the root
 README.md, please feel free to contact members of Georgian Partners. In
@@ -51,6 +51,6 @@ particular,
 * Ji Chao Zhang(@Jichaogp)
 * Christopher Choquette(@cchoquette)
 
-# Copyright
+## Copyright
 
 Copyright 2019 - Google LLC