diff --git a/tabfm/src/classifier_and_regressor.py b/tabfm/src/classifier_and_regressor.py
index 4a0ebee..97289a9 100644
--- a/tabfm/src/classifier_and_regressor.py
+++ b/tabfm/src/classifier_and_regressor.py
@@ -208,6 +208,7 @@ def transform(self, X: Any) -> np.ndarray:
 
     for i in range(n_features):
       col = X[:, i]
+      missing_mask = pd.isna(col)
       cats = self.categories_[i]
       cat_to_idx = {c: idx for idx, c in enumerate(cats)}
       mapped = pd.Series(col).map(cat_to_idx)
@@ -216,7 +217,9 @@ def transform(self, X: Any) -> np.ndarray:
       if mapped.dtype.name == "category":
         mapped = mapped.astype(object)
 
-      # Fill NaNs/Unknowns with unknown_value
+      mapped.loc[missing_mask] = self.encoded_missing_value
+
+      # Fill unknowns with unknown_value
       X_out[:, i] = mapped.fillna(self.unknown_value).values.astype(self.dtype)
 
     return X_out
@@ -239,7 +242,9 @@ def inverse_transform(self, X: np.ndarray) -> np.ndarray:
     for i in range(n_features):
       col = X[:, i]
       cats = self.categories_[i]
-      valid_mask = col != self.unknown_value
+      valid_mask = (col != self.unknown_value) & (
+          col != self.encoded_missing_value
+      )
       if np.any(valid_mask):
         indices = col[valid_mask].astype(int)
         X_out[valid_mask, i] = cats[indices]
@@ -334,7 +339,10 @@ def fit(self, X: Any, y: Any = None) -> "DatetimeTransformer":
       series = pd.to_datetime(
           X.iloc[:, pos], utc=True, errors="coerce", format="mixed"
       )
-      self._fillna_map[pos] = series.mean()
+      fillna_value = series.mean()
+      if pd.isna(fillna_value):
+        fillna_value = pd.Timestamp(0, tz="UTC")
+      self._fillna_map[pos] = fillna_value
     return self
 
   def transform(self, X: Any) -> np.ndarray:
diff --git a/tabfm/src/classifier_and_regressor_test.py b/tabfm/src/classifier_and_regressor_test.py
index 546fbee..baa52b9 100644
--- a/tabfm/src/classifier_and_regressor_test.py
+++ b/tabfm/src/classifier_and_regressor_test.py
@@ -27,6 +27,8 @@
 except ImportError:
   HAS_JAX = False
 from tabfm.src.classifier_and_regressor import _looks_like_datetime
+from tabfm.src.classifier_and_regressor import CategoricalOrdinalEncoder
+from tabfm.src.classifier_and_regressor import DatetimeTransformer
 from tabfm.src.classifier_and_regressor import EnsembleGenerator
 from tabfm.src.classifier_and_regressor import TabFMClassifier
 from tabfm.src.classifier_and_regressor import TabFMRegressor
@@ -1165,6 +1167,44 @@ def test_mostly_non_date_column_not_detected(self):
     self.assertFalse(_looks_like_datetime(pd.Series(vals, dtype="string")))
 
 
+class CategoricalOrdinalEncoderTest(absltest.TestCase):
+
+  def test_missing_and_unknown_values_are_encoded_separately(self):
+    encoder = CategoricalOrdinalEncoder(
+        unknown_value=-1, encoded_missing_value=-99
+    )
+    encoder.fit(np.array([["known"], ["other"]], dtype=object))
+
+    encoded = encoder.transform(
+        np.array([[np.nan], ["unseen"], ["known"]], dtype=object)
+    )
+
+    np.testing.assert_array_equal(encoded[:, 0], [-99, -1, 0])
+
+  def test_inverse_transform_does_not_decode_the_missing_sentinel(self):
+    encoder = CategoricalOrdinalEncoder(
+        unknown_value=-1, encoded_missing_value=-99
+    )
+    encoder.fit(np.array([["known"], ["other"]], dtype=object))
+
+    decoded = encoder.inverse_transform(np.array([[-99], [-1], [0]]))
+
+    self.assertIsNone(decoded[0, 0])
+    self.assertIsNone(decoded[1, 0])
+    self.assertEqual(decoded[2, 0], "known")
+
+
+class DatetimeTransformerTest(absltest.TestCase):
+
+  def test_all_missing_column_uses_epoch_fill_value(self):
+    X = pd.DataFrame({"ts": pd.to_datetime([None, None, None])})
+
+    transformed = DatetimeTransformer().fit_transform(X)
+
+    self.assertEqual(transformed.shape, (3, 5))
+    self.assertTrue(np.isfinite(transformed).all())
+
+
 class ColumnNameRobustnessTest(absltest.TestCase):
 
   def test_duplicate_column_names_raise_a_clear_error(self):