diff --git a/tensorflow_privacy/privacy/analysis/dp_event.py b/tensorflow_privacy/privacy/analysis/dp_event.py
index 40a11a1..5d37b43 100644
--- a/tensorflow_privacy/privacy/analysis/dp_event.py
+++ b/tensorflow_privacy/privacy/analysis/dp_event.py
@@ -59,7 +59,7 @@ incorrect results, the following should be enforced:
    is `False` when processing unknown mechanisms.
 """
 
-from typing import List
+from typing import List, Union
 
 import attr
 
@@ -115,6 +115,20 @@ class GaussianDpEvent(DpEvent):
   noise_multiplier: float
 
 
+@attr.s(frozen=True, slots=True, auto_attribs=True)
+class LaplaceDpEvent(DpEvent):
+  """Represents an application of the Laplace mechanism.
+
+  For values v_i and noise z sampled coordinate-wise from the Laplace
+  distribution L(0, s), this mechanism returns sum_i v_i + z.
+  The probability density function of the Laplace distribution L(0, s) with
+  parameter s is given as exp(-|x|/s) * (0.5/s) at x for any real value x.
+  If the L_1 norm of the values are bounded ||v_i||_1 <= C, the noise_multiplier
+  is defined as s / C.
+  """
+  noise_multiplier: float
+
+
 @attr.s(frozen=True, slots=True, auto_attribs=True)
 class SelfComposedDpEvent(DpEvent):
   """Represents repeated application of a mechanism.
@@ -176,3 +190,25 @@ class SampledWithoutReplacementDpEvent(DpEvent):
   source_dataset_size: int
   sample_size: int
   event: DpEvent
+
+
+@attr.s(frozen=True, slots=True, auto_attribs=True)
+class SingleEpochTreeAggregationDpEvent(DpEvent):
+  """Represents aggregation for a single epoch using one or more trees.
+
+  Multiple tree-aggregation steps can occur, but it is required that each
+  record occurs at most once *across all trees*. See appendix D of
+  "Practical and Private (Deep) Learning without Sampling or Shuffling"
+  https://arxiv.org/abs/2103.00039.
+
+  To represent the common case where the same record can occur in multiple
+  trees (but still at most once per tree), wrap this with `SelfComposedDpEvent`
+  or `ComposedDpEvent` and use a scalar for `step_counts`.
+
+  Attributes:
+    noise_multiplier: The ratio of the noise per node to the sensitivity.
+    step_counts: The number of steps in each tree. May be a scalar for a single
+      tree.
+  """
+  noise_multiplier: float
+  step_counts: Union[int, List[int]]
diff --git a/tensorflow_privacy/privacy/analysis/dp_event_builder_test.py b/tensorflow_privacy/privacy/analysis/dp_event_builder_test.py
index 882e4a6..6ca352c 100644
--- a/tensorflow_privacy/privacy/analysis/dp_event_builder_test.py
+++ b/tensorflow_privacy/privacy/analysis/dp_event_builder_test.py
@@ -17,6 +17,7 @@ from tensorflow_privacy.privacy.analysis import dp_event
 from tensorflow_privacy.privacy.analysis import dp_event_builder
 
 _gaussian_event = dp_event.GaussianDpEvent(1.0)
+_laplace_event = dp_event.LaplaceDpEvent(1.0)
 _poisson_event = dp_event.PoissonSampledDpEvent(_gaussian_event, 0.1)
 _self_composed_event = dp_event.SelfComposedDpEvent(_gaussian_event, 3)
 
@@ -27,11 +28,16 @@ class DpEventBuilderTest(absltest.TestCase):
     builder = dp_event_builder.DpEventBuilder()
     self.assertEqual(dp_event.NoOpDpEvent(), builder.build())
 
-  def test_single(self):
+  def test_single_gaussian(self):
     builder = dp_event_builder.DpEventBuilder()
     builder.compose(_gaussian_event)
     self.assertEqual(_gaussian_event, builder.build())
 
+  def test_single_laplace(self):
+    builder = dp_event_builder.DpEventBuilder()
+    builder.compose(_laplace_event)
+    self.assertEqual(_laplace_event, builder.build())
+
   def test_compose_no_op(self):
     builder = dp_event_builder.DpEventBuilder()
     builder.compose(dp_event.NoOpDpEvent())