diff --git a/gtsam_unstable/base/Expression-inl.h b/gtsam_unstable/base/Expression-inl.h
index de6dbeb6f..66366ae14 100644
--- a/gtsam_unstable/base/Expression-inl.h
+++ b/gtsam_unstable/base/Expression-inl.h
@@ -17,7 +17,7 @@
  * @brief Internals for Expression.h, not for general consumption
  */
 
-#include <gtsam/inference/Key.h>
+#include <gtsam/nonlinear/Values.h>
 #include <boost/foreach.hpp>
 
 namespace gtsam {
diff --git a/gtsam_unstable/base/Expression.h b/gtsam_unstable/base/Expression.h
index 24280db98..562554703 100644
--- a/gtsam_unstable/base/Expression.h
+++ b/gtsam_unstable/base/Expression.h
@@ -18,9 +18,7 @@
  */
 
 #include "Expression-inl.h"
-#include <gtsam/nonlinear/NonlinearFactor.h>
 #include <gtsam/inference/Key.h>
-#include <boost/make_shared.hpp>
 #include <boost/bind.hpp>
 
 namespace gtsam {
@@ -94,66 +92,5 @@ Expression<T> operator*(const Expression<T>& expression1,
       expression1, expression2);
 }
 
-/**
- * BAD Factor that supports arbitrary expressions via AD
- */
-template<class T>
-class BADFactor: NonlinearFactor {
-
-  const T measurement_;
-  const Expression<T> expression_;
-
-  /// get value from expression and calculate error with respect to measurement
-  Vector unwhitenedError(const Values& values) const {
-    const T& value = expression_.value(values);
-    return value.localCoordinates(measurement_);
-  }
-
-public:
-
-  /// Constructor
-  BADFactor(const T& measurement, const Expression<T>& expression) :
-      measurement_(measurement), expression_(expression) {
-  }
-  /// Constructor
-  BADFactor(const T& measurement, const ExpressionNode<T>& expression) :
-      measurement_(measurement), expression_(expression) {
-  }
-  /**
-   * Calculate the error of the factor.
-   * This is the log-likelihood, e.g. \f$ 0.5(h(x)-z)^2/\sigma^2 \f$ in case of Gaussian.
-   * In this class, we take the raw prediction error \f$ h(x)-z \f$, ask the noise model
-   * to transform it to \f$ (h(x)-z)^2/\sigma^2 \f$, and then multiply by 0.5.
-   */
-  virtual double error(const Values& values) const {
-    if (this->active(values)) {
-      const Vector e = unwhitenedError(values);
-      return 0.5 * e.squaredNorm();
-    } else {
-      return 0.0;
-    }
-  }
-
-  /// get the dimension of the factor (number of rows on linearization)
-  size_t dim() const {
-    return 0;
-  }
-
-  /// linearize to a GaussianFactor
-  boost::shared_ptr<GaussianFactor> linearize(const Values& values) const {
-    // We will construct an n-ary factor below, where  terms is a container whose
-    // value type is std::pair<Key, Matrix>, specifying the
-    // collection of keys and matrices making up the factor.
-    std::map<Key, Matrix> terms;
-    expression_.value(values, terms);
-    Vector b = unwhitenedError(values);
-    SharedDiagonal model = SharedDiagonal();
-    return boost::shared_ptr<JacobianFactor>(
-        new JacobianFactor(terms, b, model));
-  }
-
-};
-// BADFactor
-
 }
 
diff --git a/gtsam_unstable/base/tests/testBAD.cpp b/gtsam_unstable/base/tests/testBAD.cpp
index 95dd0a2de..207afa85e 100644
--- a/gtsam_unstable/base/tests/testBAD.cpp
+++ b/gtsam_unstable/base/tests/testBAD.cpp
@@ -17,7 +17,7 @@
  * @brief unit tests for Block Automatic Differentiation
  */
 
-#include <gtsam/slam/GeneralSFMFactor.h>
+#include <gtsam/geometry/PinholeCamera.h>
 #include <gtsam/geometry/Pose3.h>
 #include <gtsam/geometry/Cal3_S2.h>
 #include <gtsam_unstable/base/Expression.h>
@@ -49,19 +49,6 @@ Point2 uncalibrate(const CAL& K, const Point2& p, boost::optional<Matrix&> Dcal,
 
 TEST(BAD, test) {
 
-  // Create some values
-  Values values;
-  values.insert(1, Pose3());
-  values.insert(2, Point3(0, 0, 1));
-  values.insert(3, Cal3_S2());
-
-  // Create old-style factor to create expected value and derivatives
-  Point2 measured(-17, 30);
-  SharedNoiseModel model = noiseModel::Unit::Create(2);
-  GeneralSFMFactor2<Cal3_S2> old(measured, model, 1, 2, 3);
-  double expected_error = old.error(values);
-  GaussianFactor::shared_ptr expected = old.linearize(values);
-
   // Test Constant expression
   Expression<int> c(0);
 
@@ -81,19 +68,6 @@ TEST(BAD, test) {
   expectedKeys.insert(2);
   expectedKeys.insert(3);
   EXPECT(expectedKeys == uv_hat.keys());
-
-  // Create factor
-  BADFactor<Point2> f(measured, uv_hat);
-
-  // Check value
-  EXPECT_DOUBLES_EQUAL(expected_error, f.error(values), 1e-9);
-
-  // Check dimension
-  EXPECT_LONGS_EQUAL(0, f.dim());
-
-  // Check linearization
-  boost::shared_ptr<GaussianFactor> gf = f.linearize(values);
-  EXPECT( assert_equal(*expected, *gf, 1e-9));
 }
 
 /* ************************************************************************* */
@@ -104,20 +78,11 @@ TEST(BAD, compose) {
   Expression<Rot3> R1(1), R2(2);
   Expression<Rot3> R3 = R1 * R2;
 
-  // Create factor
-  BADFactor<Rot3> f(Rot3(), R3);
-
-  // Create some values
-  Values values;
-  values.insert(1, Rot3());
-  values.insert(2, Rot3());
-
-  // Check linearization
-  JacobianFactor expected(1, eye(3), 2, eye(3), zero(3));
-  boost::shared_ptr<GaussianFactor> gf = f.linearize(values);
-  boost::shared_ptr<JacobianFactor> jf = //
-      boost::dynamic_pointer_cast<JacobianFactor>(gf);
-  EXPECT( assert_equal(expected, *jf,1e-9));
+  // Check keys
+  std::set<Key> expectedKeys;
+  expectedKeys.insert(1);
+  expectedKeys.insert(2);
+  EXPECT(expectedKeys == R3.keys());
 }
 
 /* ************************************************************************* */
@@ -128,19 +93,10 @@ TEST(BAD, compose2) {
   Expression<Rot3> R1(1), R2(1);
   Expression<Rot3> R3 = R1 * R2;
 
-  // Create factor
-  BADFactor<Rot3> f(Rot3(), R3);
-
-  // Create some values
-  Values values;
-  values.insert(1, Rot3());
-
-  // Check linearization
-  JacobianFactor expected(1, 2*eye(3), zero(3));
-  boost::shared_ptr<GaussianFactor> gf = f.linearize(values);
-  boost::shared_ptr<JacobianFactor> jf = //
-      boost::dynamic_pointer_cast<JacobianFactor>(gf);
-  EXPECT( assert_equal(expected, *jf,1e-9));
+  // Check keys
+  std::set<Key> expectedKeys;
+  expectedKeys.insert(1);
+  EXPECT(expectedKeys == R3.keys());
 }
 
 /* ************************************************************************* */