diff --git a/gtsam/nonlinear/WhiteNoiseFactor.h b/gtsam/nonlinear/WhiteNoiseFactor.h
index 160728e2f..78e4d807d 100644
--- a/gtsam/nonlinear/WhiteNoiseFactor.h
+++ b/gtsam/nonlinear/WhiteNoiseFactor.h
@@ -75,8 +75,8 @@ namespace gtsam {
         Index j1, Index j2) {
       double e = u - z, e2 = e * e;
       double c = 2 * logSqrt2PI - log(p) + e2 * p;
-      Vector g1 = Vector_(1, -e * p);
-      Vector g2 = Vector_(1, 0.5 / p - 0.5 * e2);
+      Vector g1 = (Vec(1) << -e * p);
+      Vector g2 = (Vec(1) <<  0.5 / p - 0.5 * e2);
       Matrix G11 = Matrix_(1, 1, p);
       Matrix G12 = Matrix_(1, 1, e);
       Matrix G22 = Matrix_(1, 1, 0.5 / (p * p));
@@ -137,7 +137,7 @@ namespace gtsam {
      * TODO: Where is this used? should disappear.
      */
     virtual Vector unwhitenedError(const Values& x) const {
-      return Vector_(1, std::sqrt(2 * error(x)));
+      return (Vec(1) << std::sqrt(2 * error(x)));
     }
 
     /**
diff --git a/gtsam/nonlinear/tests/testLinearContainerFactor.cpp b/gtsam/nonlinear/tests/testLinearContainerFactor.cpp
index ed3207487..c1cd73b85 100644
--- a/gtsam/nonlinear/tests/testLinearContainerFactor.cpp
+++ b/gtsam/nonlinear/tests/testLinearContainerFactor.cpp
@@ -36,7 +36,7 @@ TEST( testLinearContainerFactor, generic_jacobian_factor ) {
   Matrix A2 = Matrix_(2,2,
       -0.0455167, -0.0443573,
       -0.0222154, -0.102489);
-  Vector b = Vector_(2, 0.0277052,
+  Vector b = (Vec(2) << 0.0277052,
       -0.0533393);
 
   JacobianFactor expLinFactor(l1, A1, l2, A2, b, diag_model2);
@@ -70,7 +70,7 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
   Matrix A2 = Matrix_(2,2,
       -0.0455167, -0.0443573,
       -0.0222154, -0.102489);
-  Vector b = Vector_(2, 0.0277052,
+  Vector b = (Vec(2) << 0.0277052,
       -0.0533393);
 
   JacobianFactor expLinFactor(l1, A1, l2, A2, b, diag_model2);
@@ -97,8 +97,8 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
   EXPECT(assert_equal(expLinPoint, *actFactor.linearizationPoint()));
 
   // Check error evaluation
-  Vector delta_l1 = Vector_(2, 1.0, 2.0);
-  Vector delta_l2 = Vector_(2, 3.0, 4.0);
+  Vector delta_l1 = (Vec(2) << 1.0, 2.0);
+  Vector delta_l2 = (Vec(2) << 3.0, 4.0);
 
   VectorValues delta = values.zeroVectors();
   delta.at(l1) = delta_l1;
@@ -130,9 +130,9 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
                             0.0, 5.0, 6.0,
                             0.0, 0.0, 9.0);
 
-  Vector g1 = Vector_(1, -7.0);
-  Vector g2 = Vector_(2, -8.0, -9.0);
-  Vector g3 = Vector_(3,  1.0,  2.0,  3.0);
+  Vector g1 = (Vec(1) << -7.0);
+  Vector g2 = (Vec(2) << -8.0, -9.0);
+  Vector g3 = (Vec(3) <<  1.0,  2.0,  3.0);
 
   double f = 10.0;
 
@@ -166,13 +166,13 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
       1.0, 2.0,
       3.0, 5.0,
       4.0, 6.0);
-  Vector g1 = Vector_(3,  1.0,  2.0,  3.0);
+  Vector g1 = (Vec(3) << 1.0,  2.0,  3.0);
 
   Matrix G22 = Matrix_(2,2,
         0.5, 0.2,
         0.0, 0.6);
 
-  Vector g2 = Vector_(2, -8.0, -9.0);
+  Vector g2 = (Vec(2) << -8.0, -9.0);
 
   double f = 10.0;
 
@@ -197,16 +197,16 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
   EXPECT(assert_equal(expLinPoints, actLinPoint));
 
   // Create delta
-  Vector delta_l1 = Vector_(2, 1.0, 2.0);
-  Vector delta_x1 = Vector_(3, 3.0, 4.0, 0.5);
-  Vector delta_x2 = Vector_(3, 6.0, 7.0, 0.3);
+  Vector delta_l1 = (Vec(2) << 1.0, 2.0);
+  Vector delta_x1 = (Vec(3) << 3.0, 4.0, 0.5);
+  Vector delta_x2 = (Vec(3) << 6.0, 7.0, 0.3);
 
   // Check error calculation
   VectorValues delta = linearizationPoint.zeroVectors();
   delta.at(l1) = delta_l1;
   delta.at(x1) = delta_x1;
   delta.at(x2) = delta_x2;
-  EXPECT(assert_equal(Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0), delta.vector(initFactor.keys())));
+  EXPECT(assert_equal((Vec(5) << 3.0, 4.0, 0.5, 1.0, 2.0), delta.vector(initFactor.keys())));
   Values noisyValues = linearizationPoint.retract(delta);
 
   double expError = initFactor.error(delta);
@@ -214,7 +214,7 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
   EXPECT_DOUBLES_EQUAL(initFactor.error(linearizationPoint.zeroVectors()), actFactor.error(linearizationPoint), tol);
 
   // Compute updated versions
-  Vector dv = Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0);
+  Vector dv = (Vec(5) << 3.0, 4.0, 0.5, 1.0, 2.0);
   Vector g(5); g << g1, g2;
   Vector g_prime = g - G.selfadjointView<Eigen::Upper>() * dv;
 
diff --git a/gtsam/nonlinear/tests/testValues.cpp b/gtsam/nonlinear/tests/testValues.cpp
index d03f78424..3093c5376 100644
--- a/gtsam/nonlinear/tests/testValues.cpp
+++ b/gtsam/nonlinear/tests/testValues.cpp
@@ -171,8 +171,8 @@ TEST(Values, expmap_a)
   config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
 
   VectorValues increment = pair_list_of
-    (key1, Vector_(3, 1.0, 1.1, 1.2))
-    (key2, Vector_(3, 1.3, 1.4, 1.5));
+    (key1, (Vec(3) << 1.0, 1.1, 1.2))
+    (key2, (Vec(3) << 1.3, 1.4, 1.5));
 
   Values expected;
   expected.insert(key1, LieVector(3, 2.0, 3.1, 4.2));
@@ -242,8 +242,8 @@ TEST(Values, localCoordinates)
   valuesA.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
 
   VectorValues expDelta = pair_list_of
-    (key1, Vector_(3, 0.1, 0.2, 0.3))
-    (key2, Vector_(3, 0.4, 0.5, 0.6));
+    (key1, (Vec(3) << 0.1, 0.2, 0.3))
+    (key2, (Vec(3) << 0.4, 0.5, 0.6));
 
   Values valuesB = valuesA.retract(expDelta);
 
@@ -275,11 +275,11 @@ TEST(Values, extract_keys)
 TEST(Values, exists_)
 {
   Values config0;
-  config0.insert(key1, LieVector(Vector_(1, 1.)));
-  config0.insert(key2, LieVector(Vector_(1, 2.)));
+  config0.insert(key1, LieVector((Vec(1) << 1.)));
+  config0.insert(key2, LieVector((Vec(1) << 2.)));
 
   boost::optional<const LieVector&> v = config0.exists<LieVector>(key1);
-  CHECK(assert_equal(Vector_(1, 1.),*v));
+  CHECK(assert_equal((Vec(1) << 1.),*v));
 }
 
 /* ************************************************************************* */