From 3eee1730ae9efaae2bd763f9835ac540b9bde02a Mon Sep 17 00:00:00 2001
From: Divyat Mahajan <divyatmahajan@gmail.com>
Date: Sun, 20 Mar 2022 15:15:32 -0400
Subject: [PATCH] Link to the privacy paper updated

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 6310d4f..281fbac 100644
--- a/README.rst
+++ b/README.rst
@@ -4,7 +4,7 @@ Toolkit for Building Robust ML models that generalize to unseen domains (RobustD
 `Shruti Tople <https://www.microsoft.com/en-us/research/people/shtople/>`_, 
 `Amit Sharma <http://www.amitsharma.in>`_
 
-`Privacy & Causal Learning (ICML 2020) <https://arxiv.org/abs/1909.12732>`_ | `MatchDG: Causal View of DG (ICML 2021) <http://proceedings.mlr.press/v139/mahajan21b.html>`_ | `Privacy & DG Connection paper <http://divy.at/privacy_dg.pdf>`_
+`Privacy & Causal Learning (ICML 2020) <https://arxiv.org/abs/1909.12732>`_ | `MatchDG: Causal View of DG (ICML 2021) <http://proceedings.mlr.press/v139/mahajan21b.html>`_ | `Privacy & DG Connection paper <https://arxiv.org/abs/2110.03369>`_
 
 For machine learning models to be reliable, they need to generalize to data
 beyond the train distribution. In addition, ML models should be robust to