diff --git a/mindarmour/diff_privacy/mechanisms/mechanisms.py b/mindarmour/diff_privacy/mechanisms/mechanisms.py
index a1103373e80aa26d2b93cabe044fac48f569c49f..f0e0063d68ced8eebdd4336b8d8ab9326b76b552 100644
--- a/mindarmour/diff_privacy/mechanisms/mechanisms.py
+++ b/mindarmour/diff_privacy/mechanisms/mechanisms.py
@@ -343,9 +343,9 @@ class _MechanismsParamsUpdater(Cell):
 class AdaClippingWithGaussianRandom(Cell):
     """
     Adaptive clipping. If `decay_policy` is 'Linear', the update formula is
-    $ norm_bound = norm_bound - learning_rate*(beta-target_unclipped_quantile)$.
-    `decay_policy` is 'Geometric', the update formula is
-    $ norm_bound = norm_bound*exp(-learning_rate*(empirical_fraction-target_unclipped_quantile))$.
+    norm_bound = norm_bound - learning_rate*(beta - target_unclipped_quantile).
+    If `decay_policy` is 'Geometric', the update formula is norm_bound =
+    norm_bound*exp(-learning_rate*(empirical_fraction - target_unclipped_quantile)).
     where beta is the empirical fraction of samples with the value at most
     `target_unclipped_quantile`.
 
@@ -355,7 +355,7 @@ class AdaClippingWithGaussianRandom(Cell):
         learning_rate(float): Learning rate of update norm clip. Default: 0.001.
         target_unclipped_quantile(float): Target quantile of norm clip. Default: 0.9.
         fraction_stddev(float): The stddev of Gaussian normal which used in
-            empirical_fraction, the formula is $empirical_fraction + N(0, fraction_stddev)$.
+            empirical_fraction, the formula is empirical_fraction + N(0, fraction_stddev).
             Default: 0.01.
         seed(int): Original random seed, if seed=0 random normal will use secure
             random number. IF seed!=0 random normal will generate values using