diff --git a/contextualized/easy/ContextualizedNetworks.py b/contextualized/easy/ContextualizedNetworks.py
index 59e792f6..956cf0b6 100644
--- a/contextualized/easy/ContextualizedNetworks.py
+++ b/contextualized/easy/ContextualizedNetworks.py
@@ -80,9 +80,9 @@ def measure_mses(self, C, X, individual_preds=False):
         mses = np.zeros((len(betas), len(C)))  # n_bootstraps x n_samples
         for i in range(X.shape[-1]):
             for j in range(X.shape[-1]):
-                residuals = np.tile(X[:, i], len(betas)) - (
-                    betas[:, :, i, j] * np.tile(X[:, j], len(betas)) + mus[:, :, i, j]
-                )
+                tiled_xi = np.array([X[:, i] for _ in range(len(betas))])
+                tiled_xj = np.array([X[:, j] for _ in range(len(betas))])
+                residuals = tiled_xi - betas[:, :, i, j]*tiled_xj + mus[:, :, i ,j]
                 mses += residuals**2 / (X.shape[-1] ** 2)
         if not individual_preds:
             mses = np.mean(mses, axis=0)
@@ -164,7 +164,7 @@ def __init__(self, **kwargs):
             **kwargs,
         )
 
-    def predict_params(self, C, individual_preds=False, **kwargs):
+    def predict_params(self, C, **kwargs):
         """
 
         :param C:
@@ -173,8 +173,12 @@ def predict_params(self, C, individual_preds=False, **kwargs):
         """
         # Returns betas
         # TODO: No mus for NOTMAD at present.
-        return super().predict_params(C, individual_preds,
-            model_includes_mus=False, **kwargs)
+        return super().predict_params(
+            C,
+            individual_preds=kwargs.get("individual_preds", False),
+            model_includes_mus=False,
+            uses_y=False,
+            project_to_dag=kwargs.get("project_to_dag", True))
         
     def predict_networks(self, C, with_offsets=False, project_to_dag=True, **kwargs):
         """