diff --git a/python/tvm/meta_schedule/adaptive_sampler.py b/python/tvm/meta_schedule/adaptive_sampler.py
new file mode 100644
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+++ b/python/tvm/meta_schedule/adaptive_sampler.py
@@ -0,0 +1,143 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+"""Sampler that uses Adaptive Sampling"""
+import numpy as np
+
+
+class AdaptiveSampler(object):
+    """Sampler that uses Adaptive sampling.
+
+    Parameters
+    ----------
+    max_sample_cnt: int
+        The maximum number of samples to be returned.
+
+    sample_cnt_step: int
+        The number of parallel measurements.
+    """
+
+    def __init__(self, max_sample_cnt, sample_cnt_step=8):
+        self.max_sample_cnt = max_sample_cnt
+        self.sample_cnt_step = sample_cnt_step
+
+    def sample(self, samples, dims, threshold=2.5, valid_dims=2):
+        """Samples using Adaptive sampling.
+
+        Parameters
+        ----------
+        samples: Array of int
+            Configs in knob form.
+
+        threshold: float
+            Hyperparameter used for adaptive sampling.
+
+        Returns
+        -------
+        Reduced set of configs in knob form.
+        """
+        cardinal_dims = np.argsort(-np.array(dims))[:valid_dims]
+        adaptive_range = np.arange(
+            self.sample_cnt_step, self.max_sample_cnt + 1, self.sample_cnt_step
+        )
+        cardinal_dims_samples = [np.array(sample)[cardinal_dims] for sample in samples]
+
+        last_loss = np.inf
+        for k in adaptive_range:
+            centroids, cluster, loss = clusterize(cardinal_dims_samples, k)
+
+            if loss >= last_loss / threshold:
+                break
+            else:
+                last_loss = loss
+
+        reduced_samples = reduce_samples(samples, centroids, cluster, dims, cardinal_dims)
+
+        return reduced_samples
+
+
+def distance(x, y, l=2):
+    """calculates distance between two points."""
+    return np.sum((x - y) ** l).astype(float)
+
+
+def clusterize(points, k, max_iter=100):
+    """k-means clustering algorithm."""
+    centroids = [points[i] for i in np.random.randint(len(points), size=k)]
+    new_assignment = [0] * len(points)
+    old_assignment = [-1] * len(points)
+
+    i = 0
+    split = False
+    while i < max_iter or split == True and new_assignment != old_assignment:
+        old_assignment = list(new_assignment)
+        split = False
+        i += 1
+
+        for p in range(len(points)):
+            distances = [distance(points[p], centroids[c]) for c in range(len(centroids))]
+            new_assignment[p] = np.argmin(distances)
+
+        for c in range(len(centroids)):
+            members = [points[p] for p in range(len(points)) if new_assignment[p] == c]
+
+            if members:
+                centroids[c] = np.mean(members, axis=0).astype(int)
+
+            else:
+                centroids[c] = points[np.random.choice(len(points))]
+                split = True
+
+    loss = np.sum([distance(points[p], centroids[new_assignment[p]])])
+    return centroids, new_assignment, loss
+
+
+def synthesize_sample(centroid, sample_dims, dims, cardinal_dims, reduction="sample"):
+    """Sample synthesis."""
+    sample = []
+    for d in range(len(dims)):
+        if d in cardinal_dims:
+            sample.append(centroid[list(cardinal_dims).index(d)])
+
+        else:
+            if reduction == "sample":
+                sample.append(np.random.choice(sample_dims[d]))
+            elif reduction == "mode":
+                sample.append(max(set(sample_dims[d]), key=sample_dims[d].count))
+    return sample
+
+
+def reduce_samples(samples, centroids, cluster, dims, cardinal_dims):
+    """reduce samples to ones that subsumes the input samples."""
+    reduced_samples = []
+    for c in range(len(centroids)):
+        members = [samples[s] for s in range(len(samples)) if cluster[s] == c]
+        sample_dims = []
+        if not members:
+            sample_dims = [list(range(dims[d])) for d in range(len(dims))]
+
+        else:
+            sample_dims = [[s[d] for s in samples] for d in range(len(dims))]
+
+        unique = False
+        while unique is False:
+            new_sample = synthesize_sample(centroids[c], sample_dims, dims, cardinal_dims)
+
+            if new_sample not in reduced_samples:
+                reduced_samples.append(new_sample)
+                unique = True
+
+    return reduced_samples