diff --git a/CHANGELOG.md b/CHANGELOG.md
index 67654176f6..dfbd8a1015 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,11 +1,11 @@
 # v0.2.2
 
 This version adds support for PyTorch 1.4.0. There are also several minor feature improvements and bug fixes listed below.
-
-- added option for neutral elements to be used in the place of empty tensors in reduction operations (`operations.__reduce_op`) (cf. [#369](https://github.com/helmholtz-analytics/heat/issues/369) and [#444](https://github.com/helmholtz-analytics/heat/issues/444))
-- `var` and `std` both now support iterable axis arguments
-- updated pull request template
-- bug fix: `x.unique()` returns a DNDarray both in distributed and non-distributed mode (cf. [#464])
+- [#443](https://github.com/helmholtz-analytics/heat/pull/443) added option for neutral elements to be used in the place of empty tensors in reduction operations (`operations.__reduce_op`) (cf. [#369](https://github.com/helmholtz-analytics/heat/issues/369) and [#444](https://github.com/helmholtz-analytics/heat/issues/444))
+- [#445](https://github.com/helmholtz-analytics/heat/pull/445) `var` and `std` both now support iterable axis arguments
+- [#452](https://github.com/helmholtz-analytics/heat/pull/452) updated pull request template
+- [#465](https://github.com/helmholtz-analytics/heat/pull/465) bug fix: `x.unique()` returns a DNDarray both in distributed and non-distributed mode (cf. [#464])
+- [#463](https://github.com/helmholtz-analytics/heat/pull/463) Bugfix: Lasso tests now run with both GPUs and CPUs
 
 # v0.2.1
 
diff --git a/heat/core/regression/lasso/tests/test_lasso.py b/heat/core/regression/lasso/tests/test_lasso.py
index 64cb5a4ab7..69c3bc3950 100644
--- a/heat/core/regression/lasso/tests/test_lasso.py
+++ b/heat/core/regression/lasso/tests/test_lasso.py
@@ -24,10 +24,14 @@ def test_lasso(self):
             # ToDo: add additional tests
             # get some test data
             X = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="x",
+                device=ht_device,
             )
             y = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="y",
+                device=ht_device,
             )
 
             # normalize dataset
@@ -60,15 +64,19 @@ def test_lasso(self):
             self.assertEqual(yest.shape, (m,))
 
             X = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="x",
+                device=ht_device,
             )
             y = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="y",
+                device=ht_device,
             )
 
             # Now the same stuff again in PyTorch
-            X = torch.tensor(X._DNDarray__array)
-            y = torch.tensor(y._DNDarray__array)
+            X = torch.tensor(X._DNDarray__array, device=device)
+            y = torch.tensor(y._DNDarray__array, device=device)
 
             # normalize dataset
             X = X / torch.sqrt((torch.mean(X ** 2, 0)))
@@ -100,15 +108,19 @@ def test_lasso(self):
             self.assertEqual(yest.shape, (m,))
 
             X = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="x",
+                device=ht_device,
             )
             y = ht.load_hdf5(
-                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
+                os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
+                dataset="y",
+                device=ht_device,
             )
 
             # Now the same stuff again in PyTorch
-            X = X._DNDarray__array.numpy()
-            y = y._DNDarray__array.numpy()
+            X = X._DNDarray__array.cpu().numpy()
+            y = y._DNDarray__array.cpu().numpy()
 
             # normalize dataset
             X = X / np.sqrt((np.mean(X ** 2, axis=0, keepdims=True)))