diff --git a/tensorflow_privacy/BUILD b/tensorflow_privacy/BUILD
index 4bcd1b2..0d9f2f4 100644
--- a/tensorflow_privacy/BUILD
+++ b/tensorflow_privacy/BUILD
@@ -26,6 +26,7 @@ py_library(
         "//tensorflow_privacy/privacy/dp_query:no_privacy_query",
         "//tensorflow_privacy/privacy/dp_query:normalized_query",
         "//tensorflow_privacy/privacy/dp_query:quantile_adaptive_clip_sum_query",
+        "//tensorflow_privacy/privacy/dp_query:quantile_adaptive_clip_tree_query",
         "//tensorflow_privacy/privacy/dp_query:quantile_estimator_query",
         "//tensorflow_privacy/privacy/dp_query:restart_query",
         "//tensorflow_privacy/privacy/dp_query:tree_aggregation",
diff --git a/tensorflow_privacy/__init__.py b/tensorflow_privacy/__init__.py
index c4aee26..6cb497f 100644
--- a/tensorflow_privacy/__init__.py
+++ b/tensorflow_privacy/__init__.py
@@ -45,6 +45,7 @@ else:
   from tensorflow_privacy.privacy.dp_query.quantile_estimator_query import QuantileEstimatorQuery
   from tensorflow_privacy.privacy.dp_query.quantile_estimator_query import NoPrivacyQuantileEstimatorQuery
   from tensorflow_privacy.privacy.dp_query.quantile_adaptive_clip_sum_query import QuantileAdaptiveClipSumQuery
+  from tensorflow_privacy.privacy.dp_query.quantile_adaptive_clip_tree_query import QAdaClipTreeResSumQuery
   from tensorflow_privacy.privacy.dp_query import restart_query
   from tensorflow_privacy.privacy.dp_query import tree_aggregation
   from tensorflow_privacy.privacy.dp_query.restart_query import RestartQuery
diff --git a/tensorflow_privacy/privacy/dp_query/BUILD b/tensorflow_privacy/privacy/dp_query/BUILD
index 9fed57d..c392760 100644
--- a/tensorflow_privacy/privacy/dp_query/BUILD
+++ b/tensorflow_privacy/privacy/dp_query/BUILD
@@ -323,3 +323,30 @@ py_library(
     srcs_version = "PY3",
     deps = [":dp_query"],
 )
+
+py_library(
+    name = "quantile_adaptive_clip_tree_query",
+    srcs = ["quantile_adaptive_clip_tree_query.py"],
+    srcs_version = "PY3",
+    deps = [
+        ":dp_query",
+        ":quantile_estimator_query",
+        ":tree_aggregation_query",
+        "//third_party/py/tensorflow:tensorflow_no_contrib",
+        "@com_google_differential_py//dp_accounting:accounting",
+    ],
+)
+
+py_test(
+    name = "quantile_adaptive_clip_tree_query_test",
+    timeout = "long",
+    srcs = ["quantile_adaptive_clip_tree_query_test.py"],
+    python_version = "PY3",
+    shard_count = 5,
+    srcs_version = "PY3",
+    deps = [
+        ":quantile_adaptive_clip_tree_query",
+        ":test_utils",
+        "//third_party/py/tensorflow:tensorflow_no_contrib",
+    ],
+)
diff --git a/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query.py b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query.py
new file mode 100644
index 0000000..63590a0
--- /dev/null
+++ b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query.py
@@ -0,0 +1,196 @@
+# Copyright 2021, The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""`DPQuery` for tree aggregation queries with adaptive clipping."""
+
+import collections
+
+import dp_accounting
+import tensorflow as tf
+from tensorflow_privacy.privacy.dp_query import dp_query
+from tensorflow_privacy.privacy.dp_query import quantile_estimator_query
+from tensorflow_privacy.privacy.dp_query import tree_aggregation_query
+
+
+class QAdaClipTreeResSumQuery(dp_query.SumAggregationDPQuery):
+  """`DPQuery` for tree aggregation queries with adaptive clipping.
+
+  The implementation is based on tree aggregation noise for cumulative sum in
+  "Practical and Private (Deep) Learning without Sampling or Shuffling"
+  (https://arxiv.org/abs/2103.00039) and quantile-based adaptive clipping in
+  "Differentially Private Learning with Adaptive Clipping"
+  (https://arxiv.org/abs/1905.03871).
+
+  The quantile value will be continuously estimated, but the clip norm is only
+  updated when `reset_state` is called, and the tree state will be reset. This
+  will force the clip norm (and corresponding stddev) in a tree unchanged.
+  """
+
+  # pylint: disable=invalid-name
+  _GlobalState = collections.namedtuple(
+      '_GlobalState',
+      ['noise_multiplier', 'sum_state', 'quantile_estimator_state'])
+
+  # pylint: disable=invalid-name
+  _SampleState = collections.namedtuple(
+      '_SampleState', ['sum_state', 'quantile_estimator_state'])
+
+  # pylint: disable=invalid-name
+  _SampleParams = collections.namedtuple(
+      '_SampleParams', ['sum_params', 'quantile_estimator_params'])
+
+  def __init__(self,
+               initial_l2_norm_clip,
+               noise_multiplier,
+               record_specs,
+               target_unclipped_quantile,
+               learning_rate,
+               clipped_count_stddev,
+               expected_num_records,
+               geometric_update=True,
+               noise_seed=None):
+    """Initializes the `QAdaClipTreeResSumQuery`.
+
+    Args:
+      initial_l2_norm_clip: The initial value of clipping norm.
+      noise_multiplier: The stddev of the noise added to the output will be this
+        times the current value of the clipping norm.
+      record_specs: A nested structure of `tf.TensorSpec`s specifying structure
+        and shapes of records.
+      target_unclipped_quantile: The desired quantile of updates which should be
+        unclipped. I.e., a value of 0.8 means a value of l2_norm_clip should be
+        found for which approximately 20% of updates are clipped each round.
+        Andrew et al. recommends that this be set to 0.5 to clip to the median.
+      learning_rate: The learning rate for the clipping norm adaptation. With
+        geometric updating, a rate of r means that the clipping norm will change
+        by a maximum factor of exp(r) at each round. This maximum is attained
+        when |actual_unclipped_fraction - target_unclipped_quantile| is 1.0.
+        Andrew et al. recommends that this be set to 0.2 for geometric updating.
+      clipped_count_stddev: The stddev of the noise added to the clipped_count.
+        Andrew et al. recommends that this be set to `expected_num_records / 20`
+        for reasonably fast adaptation and high privacy.
+      expected_num_records: The expected number of records per round, used to
+        estimate the clipped count quantile.
+      geometric_update: If `True`, use geometric updating of clip (recommended).
+      noise_seed: Integer seed for the Gaussian noise generator of
+        `TreeResidualSumQuery`. If `None`, a nondeterministic seed based on
+        system time will be generated.
+    """
+    self._noise_multiplier = noise_multiplier
+
+    self._quantile_estimator_query = (
+        quantile_estimator_query.TreeQuantileEstimatorQuery(
+            initial_l2_norm_clip,
+            target_unclipped_quantile,
+            learning_rate,
+            clipped_count_stddev,
+            expected_num_records,
+            geometric_update,
+        )
+    )
+
+    self._sum_query = (
+        tree_aggregation_query.TreeResidualSumQuery.build_l2_gaussian_query(
+            initial_l2_norm_clip,
+            noise_multiplier,
+            record_specs,
+            noise_seed=noise_seed,
+            use_efficient=True,
+        )
+    )
+
+    assert isinstance(self._sum_query, dp_query.SumAggregationDPQuery)
+    assert isinstance(self._quantile_estimator_query,
+                      dp_query.SumAggregationDPQuery)
+
+  def initial_global_state(self):
+    """Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
+    return self._GlobalState(
+        tf.cast(self._noise_multiplier, tf.float32),
+        self._sum_query.initial_global_state(),
+        self._quantile_estimator_query.initial_global_state())
+
+  def derive_sample_params(self, global_state):
+    """Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
+    return self._SampleParams(
+        self._sum_query.derive_sample_params(global_state.sum_state),
+        self._quantile_estimator_query.derive_sample_params(
+            global_state.quantile_estimator_state))
+
+  def initial_sample_state(self, template):
+    """Implements `tensorflow_privacy.DPQuery.initial_sample_state`."""
+    return self._SampleState(
+        self._sum_query.initial_sample_state(template),
+        self._quantile_estimator_query.initial_sample_state())
+
+  def preprocess_record(self, params, record):
+    """Implements `tensorflow_privacy.DPQuery.preprocess_record`."""
+    clipped_record, global_norm = (
+        self._sum_query.preprocess_record_l2_impl(params.sum_params, record))
+
+    below_estimate = self._quantile_estimator_query.preprocess_record(
+        params.quantile_estimator_params, global_norm)
+
+    return self._SampleState(clipped_record, below_estimate)
+
+  def get_noised_result(self, sample_state, global_state):
+    """Implements `tensorflow_privacy.DPQuery.get_noised_result`."""
+    noised_vectors, sum_state, sum_event = self._sum_query.get_noised_result(
+        sample_state.sum_state, global_state.sum_state)
+
+    _, quantile_estimator_state, quantile_event = (
+        self._quantile_estimator_query.get_noised_result(
+            sample_state.quantile_estimator_state,
+            global_state.quantile_estimator_state))
+
+    new_global_state = self._GlobalState(global_state.noise_multiplier,
+                                         sum_state, quantile_estimator_state)
+    event = dp_accounting.ComposedDpEvent(events=[sum_event, quantile_event])
+    return noised_vectors, new_global_state, event
+
+  def reset_state(self, noised_results, global_state):
+    """Returns state after resetting the tree and updating the clip norm.
+
+    This function will be used in `restart_query.RestartQuery` after calling
+    `get_noised_result` when the restarting condition is met. The clip norm (
+    and corresponding noise stddev) for the tree aggregated sum query is only
+    updated from the quantile-based estimation when `reset_state` is called.
+
+    Args:
+      noised_results: Noised cumulative sum returned by `get_noised_result`.
+      global_state: Updated global state returned by `get_noised_result`, which
+        records noise for the conceptual cumulative sum of the current leaf
+        node, and tree state for the next conceptual cumulative sum.
+
+    Returns:
+      New global state with restarted tree state, and new clip norm.
+    """
+    new_l2_norm_clip = tf.math.maximum(
+        global_state.quantile_estimator_state.current_estimate, 0.0)
+    new_sum_stddev = new_l2_norm_clip * global_state.noise_multiplier
+    sum_state = self._sum_query.reset_l2_clip_gaussian_noise(
+        global_state.sum_state,
+        clip_norm=new_l2_norm_clip,
+        stddev=new_sum_stddev)
+    sum_state = self._sum_query.reset_state(noised_results, sum_state)
+    quantile_estimator_state = self._quantile_estimator_query.reset_state(
+        noised_results, global_state.quantile_estimator_state)
+
+    return global_state._replace(
+        sum_state=sum_state, quantile_estimator_state=quantile_estimator_state)
+
+  def derive_metrics(self, global_state):
+    """Returns the clipping norm and estimated quantile value as a metric."""
+    return collections.OrderedDict(
+        current_clip=global_state.sum_state.clip_value,
+        estimate_clip=global_state.quantile_estimator_state.current_estimate)
diff --git a/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query_test.py b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query_test.py
new file mode 100644
index 0000000..970116f
--- /dev/null
+++ b/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_tree_query_test.py
@@ -0,0 +1,469 @@
+# Copyright 2021, The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+from tensorflow_privacy.privacy.dp_query import quantile_adaptive_clip_tree_query
+from tensorflow_privacy.privacy.dp_query import test_utils
+
+
+class QAdaClipTreeResSumQueryTest(tf.test.TestCase, parameterized.TestCase):
+
+  def test_sum_no_clip_no_noise(self):
+    record1 = tf.constant([2.0, 0.0])
+    record2 = tf.constant([-1.0, 1.0])
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=10.0,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([2]),
+        target_unclipped_quantile=1.0,
+        learning_rate=0.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0)
+    query_result, _ = test_utils.run_query(query, [record1, record2])
+    result = query_result.numpy()
+    expected = [1.0, 1.0]
+    self.assertAllClose(result, expected)
+
+  def test_sum_with_clip_no_noise(self):
+    record1 = tf.constant([-6.0, 8.0])  # Clipped to [-3.0, 4.0].
+    record2 = tf.constant([4.0, -3.0])  # Not clipped.
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=5.0,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([2]),
+        target_unclipped_quantile=1.0,
+        learning_rate=0.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0)
+
+    query_result, _ = test_utils.run_query(query, [record1, record2])
+    result = query_result.numpy()
+    expected = [1.0, 1.0]
+    self.assertAllClose(result, expected)
+
+  def test_sum_with_noise(self):
+    vector_size = 1000
+    record1 = tf.constant(2.71828, shape=[vector_size])
+    record2 = tf.constant(3.14159, shape=[vector_size])
+    stddev = 1.0
+    clip = 5.0
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=clip,
+        noise_multiplier=stddev / clip,
+        record_specs=tf.TensorSpec([vector_size]),
+        target_unclipped_quantile=1.0,
+        learning_rate=0.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0,
+        noise_seed=1)
+
+    query_result, _ = test_utils.run_query(query, [record1, record2])
+
+    result_stddev = np.std(query_result.numpy())
+    self.assertNear(result_stddev, stddev, 0.1)
+
+  def _test_estimate_clip_expected_sum(self,
+                                       query,
+                                       global_state,
+                                       records,
+                                       expected_sums,
+                                       expected_clips,
+                                       reset=True):
+    for expected_sum, expected_clip in zip(expected_sums, expected_clips):
+      initial_clip = global_state.sum_state.clip_value
+      actual_sum, global_state = test_utils.run_query(query, records,
+                                                      global_state)
+      if reset:
+        global_state = query.reset_state(actual_sum, global_state)
+        actual_clip = global_state.sum_state.clip_value
+        self.assertAllClose(actual_clip.numpy(), expected_clip)
+        self.assertAllClose(actual_sum.numpy(), (expected_sum,))
+      else:
+        actual_clip = global_state.sum_state.clip_value
+        estimate_clip = global_state.quantile_estimator_state.current_estimate
+        self.assertAllClose(actual_clip.numpy(), initial_clip)
+        self.assertAllClose(estimate_clip.numpy(), expected_clip)
+        self.assertAllClose(actual_sum.numpy(), (expected_sums[0],))
+
+  @parameterized.named_parameters(('adaptive', True), ('constant', False))
+  def test_adaptation_target_zero(self, reset):
+    record1 = tf.constant([8.5])
+    record2 = tf.constant([-7.25])
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=10.0,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=0.0,
+        learning_rate=1.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0,
+        geometric_update=False)
+
+    global_state = query.initial_global_state()
+
+    initial_clip = global_state.sum_state.clip_value
+    self.assertAllClose(initial_clip, 10.0)
+
+    # On the first two iterations, nothing is clipped, so the clip goes down
+    # by 1.0 (the learning rate). When the clip reaches 8.0, one record is
+    # clipped, so the clip goes down by only 0.5. After two more iterations,
+    # both records are clipped, and the clip norm stays there (at 7.0).
+
+    expected_sums = [1.25, 1.25, 0.75, 0.25, 0.0]
+    expected_clips = [9.0, 8.0, 7.5, 7.0, 7.0]
+    self._test_estimate_clip_expected_sum(
+        query,
+        global_state, [record1, record2],
+        expected_sums,
+        expected_clips,
+        reset=reset)
+
+  @parameterized.named_parameters(('adaptive', True), ('constant', False))
+  def test_adaptation_target_zero_geometric(self, reset):
+    record1 = tf.constant([5.0])
+    record2 = tf.constant([-2.5])
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=16.0,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=0.0,
+        learning_rate=np.log(2.0),  # Geometric steps in powers of 2.
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0,
+        geometric_update=True)
+
+    global_state = query.initial_global_state()
+
+    initial_clip = global_state.sum_state.clip_value
+    self.assertAllClose(initial_clip, 16.0)
+
+    # For two iterations, nothing is clipped, so the clip is cut in half.
+    # Then one record is clipped, so the clip goes down by only sqrt(2.0) to
+    # 4 / sqrt(2.0). Still only one record is clipped, so it reduces to 2.0.
+    # Now both records are clipped, and the clip norm stays there (at 2.0).
+
+    four_div_root_two = 4 / np.sqrt(2.0)  # approx 2.828
+
+    expected_sums = [2.5, 2.5, 1.5, four_div_root_two - 2.5, 0.0]
+    expected_clips = [8.0, 4.0, four_div_root_two, 2.0, 2.0]
+    self._test_estimate_clip_expected_sum(
+        query,
+        global_state, [record1, record2],
+        expected_sums,
+        expected_clips,
+        reset=reset)
+
+  @parameterized.named_parameters(('adaptive', True), ('constant', False))
+  def test_adaptation_target_one(self, reset):
+    record1 = tf.constant([-1.5])
+    record2 = tf.constant([2.75])
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=0.0,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=1.0,
+        learning_rate=1.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0,
+        geometric_update=False)
+
+    global_state = query.initial_global_state()
+
+    initial_clip = global_state.sum_state.clip_value
+    self.assertAllClose(initial_clip, 0.0)
+
+    # On the first two iterations, both are clipped, so the clip goes up
+    # by 1.0 (the learning rate). When the clip reaches 2.0, only one record is
+    # clipped, so the clip goes up by only 0.5. After two more iterations,
+    # both records are clipped, and the clip norm stays there (at 3.0).
+
+    expected_sums = [0.0, 0.0, 0.5, 1.0, 1.25]
+    expected_clips = [1.0, 2.0, 2.5, 3.0, 3.0]
+    self._test_estimate_clip_expected_sum(
+        query,
+        global_state, [record1, record2],
+        expected_sums,
+        expected_clips,
+        reset=reset)
+
+  @parameterized.named_parameters(('adaptive', True), ('constant', False))
+  def test_adaptation_target_one_geometric(self, reset):
+    record1 = tf.constant([-1.5])
+    record2 = tf.constant([3.0])
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=0.5,
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=1.0,
+        learning_rate=np.log(2.0),  # Geometric steps in powers of 2.
+        clipped_count_stddev=0.0,
+        expected_num_records=2.0,
+        geometric_update=True)
+
+    global_state = query.initial_global_state()
+
+    initial_clip = global_state.sum_state.clip_value
+    self.assertAllClose(initial_clip, 0.5)
+
+    # On the first two iterations, both are clipped, so the clip is doubled.
+    # When the clip reaches 2.0, only one record is clipped, so the clip is
+    # multiplied by sqrt(2.0). Still only one is clipped so it increases to 4.0.
+    # Now both records are clipped, and the clip norm stays there (at 4.0).
+
+    two_times_root_two = 2 * np.sqrt(2.0)  # approx 2.828
+
+    expected_sums = [0.0, 0.0, 0.5, two_times_root_two - 1.5, 1.5]
+    expected_clips = [1.0, 2.0, two_times_root_two, 4.0, 4.0]
+    self._test_estimate_clip_expected_sum(
+        query,
+        global_state, [record1, record2],
+        expected_sums,
+        expected_clips,
+        reset=reset)
+
+  def _test_estimate_clip_converge(self,
+                                   query,
+                                   records,
+                                   expected_clip,
+                                   tolerance,
+                                   learning_rate=None,
+                                   total_steps=50,
+                                   converge_steps=40):
+    global_state = query.initial_global_state()
+    for t in range(total_steps):
+      if learning_rate is not None:
+        learning_rate.assign(1.0 / np.sqrt(t + 1))
+      actual_sum, global_state = test_utils.run_query(query, records,
+                                                      global_state)
+      if t > converge_steps:
+        global_state = query.reset_state(actual_sum, global_state)
+        estimate_clip = global_state.sum_state.clip_value
+        self.assertNear(estimate_clip, expected_clip, tolerance)
+
+  @parameterized.named_parameters(('start_low_arithmetic', True, False),
+                                  ('start_low_geometric', True, True),
+                                  ('start_high_arithmetic', False, False),
+                                  ('start_high_geometric', False, True))
+  def test_adaptation_linspace(self, start_low, geometric):
+    # `num_records` records equally spaced from 0 to 10 in 0.1 increments.
+    # Test that we converge to the correct median value and bounce around it.
+    num_records = 21
+    records = [
+        tf.constant(x)
+        for x in np.linspace(0.0, 10.0, num=num_records, dtype=np.float32)
+    ]
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=(1.0 if start_low else 10.0),
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=0.5,
+        learning_rate=1.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=num_records,
+        geometric_update=geometric)
+
+    self._test_estimate_clip_converge(
+        query, records, expected_clip=5., tolerance=0.25)
+
+  @parameterized.named_parameters(('start_low_arithmetic', True, False),
+                                  ('start_low_geometric', True, True),
+                                  ('start_high_arithmetic', False, False),
+                                  ('start_high_geometric', False, True))
+  def test_adaptation_all_equal(self, start_low, geometric):
+    # `num_records` equal records. Test that we converge to that record and
+    # bounce around it. Unlike the linspace test, the quantile-matching
+    # objective is very sharp at the optimum so a decaying learning rate is
+    # necessary.
+    num_records = 20
+    records = [tf.constant(5.0)] * num_records
+
+    learning_rate = tf.Variable(1.0)
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=(1.0 if start_low else 10.0),
+        noise_multiplier=0.0,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=0.5,
+        learning_rate=learning_rate,
+        clipped_count_stddev=0.0,
+        expected_num_records=num_records,
+        geometric_update=geometric)
+
+    self._test_estimate_clip_converge(
+        query,
+        records,
+        expected_clip=5.,
+        tolerance=0.5,
+        learning_rate=learning_rate)
+
+  def _test_noise_multiplier(self,
+                             query,
+                             records,
+                             noise_multiplier,
+                             learning_rate=None,
+                             tolerance=0.15,
+                             total_steps=10):
+    global_state = query.initial_global_state()
+    for t in range(total_steps):
+      if learning_rate is not None:
+        learning_rate.assign((t + 1.)**(-.5))
+      params = query.derive_sample_params(global_state)
+      sample_state = query.initial_sample_state(records[0])
+      for record in records:
+        sample_state = query.accumulate_record(params, sample_state, record)
+      actual_sum, global_state, _ = query.get_noised_result(
+          sample_state, global_state)
+      expected_std = global_state.sum_state.clip_value * noise_multiplier
+      self.assertAllClose(
+          expected_std,
+          global_state.sum_state.tree_state.value_generator_state.stddev)
+      global_state = query.reset_state(actual_sum, global_state)
+      self.assertAllClose(
+          expected_std, tf.math.reduce_std(actual_sum), rtol=tolerance)
+
+  @parameterized.named_parameters(('start_low_arithmetic', True, False),
+                                  ('start_low_geometric', True, True),
+                                  ('start_high_arithmetic', False, False),
+                                  ('start_high_geometric', False, True))
+  def test_adaptation_linspace_noise(self, start_low, geometric):
+    # `num_records` records equally spaced from 0 to 10 in 0.1 increments.
+    # Test that we converge to the correct median value and bounce around it.
+    num_records, vector_size, noise_multiplier = 11, 1000, 0.1
+    records = [
+        tf.constant(
+            vector_size**(-.5) * x, shape=[vector_size], dtype=tf.float32)
+        for x in np.linspace(0.0, 10.0, num=num_records)
+    ]
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=(1.0 if start_low else 10.0),
+        noise_multiplier=noise_multiplier,
+        record_specs=tf.TensorSpec([vector_size]),
+        target_unclipped_quantile=0.5,
+        learning_rate=1.0,
+        clipped_count_stddev=0.0,
+        expected_num_records=num_records,
+        geometric_update=geometric,
+        noise_seed=1)
+
+    self._test_noise_multiplier(query, records, noise_multiplier)
+
+  @parameterized.named_parameters(('start_low_arithmetic', True, False),
+                                  ('start_low_geometric', True, True),
+                                  ('start_high_arithmetic', False, False),
+                                  ('start_high_geometric', False, True))
+  def test_adaptation_all_equal_noise(self, start_low, geometric):
+    # `num_records` equal records. Test that we converge to that record and
+    # bounce around it. Unlike the linspace test, the quantile-matching
+    # objective is very sharp at the optimum so a decaying learning rate is
+    # necessary.
+    num_records, vector_size, noise_multiplier = 10, 1000, 0.5
+    records = [
+        tf.constant(
+            vector_size**(-.5) * 5., shape=[vector_size], dtype=tf.float32)
+    ] * num_records
+
+    learning_rate = tf.Variable(1.)
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=(1.0 if start_low else 10.0),
+        noise_multiplier=noise_multiplier,
+        record_specs=tf.TensorSpec([vector_size]),
+        target_unclipped_quantile=0.5,
+        learning_rate=learning_rate,
+        clipped_count_stddev=0.0,
+        expected_num_records=num_records,
+        geometric_update=geometric,
+        noise_seed=1)
+
+    self._test_noise_multiplier(
+        query, records, noise_multiplier, learning_rate=learning_rate)
+
+  def test_adaptation_clip_noise(self):
+    sample_num, tolerance, stddev = 1000, 0.3, 0.1
+    initial_clip, expected_num_records = 5., 2.
+    record1 = tf.constant(1.)
+    record2 = tf.constant(10.)
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=initial_clip,
+        noise_multiplier=0.,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=.5,
+        learning_rate=1.,
+        clipped_count_stddev=stddev,
+        expected_num_records=expected_num_records,
+        geometric_update=False,
+        noise_seed=1)
+
+    global_state = query.initial_global_state()
+    samples = []
+    for _ in range(sample_num):
+      noised_results, global_state = test_utils.run_query(
+          query, [record1, record2], global_state)
+      samples.append(noised_results.numpy())
+      global_state = query.reset_state(noised_results, global_state)
+      self.assertNotEqual(
+          global_state.quantile_estimator_state.current_estimate, initial_clip)
+      # Force to use the same clip norm for noise estimation
+      quantile_estimator_state = global_state.quantile_estimator_state._replace(
+          current_estimate=initial_clip)
+      global_state = global_state._replace(
+          quantile_estimator_state=quantile_estimator_state)
+
+    # The sum result is 1. (unclipped) + 5. (clipped) = 6.
+    self.assertAllClose(np.mean(samples), 6., atol=4 * stddev)
+    self.assertAllClose(
+        np.std(samples), stddev / expected_num_records, rtol=tolerance)
+
+  @parameterized.named_parameters(('start_low_arithmetic', True, False),
+                                  ('start_low_geometric', True, True),
+                                  ('start_high_arithmetic', False, False),
+                                  ('start_high_geometric', False, True))
+  def test_adaptation_linspace_noise_converge(self, start_low, geometric):
+    # `num_records` records equally spaced from 0 to 10 in 0.1 increments.
+    # Test that we converge to the correct median value and bounce around it.
+    num_records = 21
+    records = [
+        tf.constant(x)
+        for x in np.linspace(0.0, 10.0, num=num_records, dtype=np.float32)
+    ]
+
+    query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
+        initial_l2_norm_clip=(1.0 if start_low else 10.0),
+        noise_multiplier=0.01,
+        record_specs=tf.TensorSpec([]),
+        target_unclipped_quantile=0.5,
+        learning_rate=1.0,
+        clipped_count_stddev=0.01,
+        expected_num_records=num_records,
+        geometric_update=geometric,
+        noise_seed=1)
+
+    self._test_estimate_clip_converge(
+        query, records, expected_clip=5., tolerance=0.25)
+
+
+if __name__ == '__main__':
+  tf.test.main()