diff --git a/gtsam.h b/gtsam.h
index 1094d9dd9..8ee778f4c 100644
--- a/gtsam.h
+++ b/gtsam.h
@@ -1458,7 +1458,7 @@ virtual class Null: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class Fair: gtsam::noiseModel::mEstimator::Base {
@@ -1469,7 +1469,7 @@ virtual class Fair: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class Huber: gtsam::noiseModel::mEstimator::Base {
@@ -1480,7 +1480,7 @@ virtual class Huber: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
@@ -1491,7 +1491,7 @@ virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
@@ -1502,7 +1502,7 @@ virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
@@ -1513,7 +1513,7 @@ virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
@@ -1524,7 +1524,7 @@ virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class DCS: gtsam::noiseModel::mEstimator::Base {
@@ -1535,7 +1535,7 @@ virtual class DCS: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
@@ -1546,7 +1546,7 @@ virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
   void serializable() const;
 
   double weight(double error) const;
-  double loss(double error) const;
+  double residual(double error) const;
 };
 
 }///\namespace mEstimator
diff --git a/gtsam/geometry/tests/testPose3.cpp b/gtsam/geometry/tests/testPose3.cpp
index 8433bbb01..ec0450abb 100644
--- a/gtsam/geometry/tests/testPose3.cpp
+++ b/gtsam/geometry/tests/testPose3.cpp
@@ -1008,7 +1008,14 @@ TEST(Pose3, print) {
   // Generate the expected output
   std::stringstream expected;
   Point3 translation(1, 2, 3);
+
+#ifdef GTSAM_TYPEDEF_POINTS_TO_VECTORS
+  expected << "1\n"
+              "2\n"
+              "3;\n";
+#else
   expected << '[' << translation.x() << ", " << translation.y() << ", " << translation.z() << "]\';";
+#endif
 
   // reset cout to the original stream
   std::cout.rdbuf(oldbuf);
diff --git a/gtsam/linear/LossFunctions.h b/gtsam/linear/LossFunctions.h
index 8d569a5df..d1c3adb35 100644
--- a/gtsam/linear/LossFunctions.h
+++ b/gtsam/linear/LossFunctions.h
@@ -36,12 +36,12 @@ namespace noiseModel {
  * The mEstimator name space contains all robust error functions.
  * It mirrors the exposition at
  *  https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
- * which talks about minimizing \sum \rho(r_i), where \rho is a loss function of choice.
+ * which talks about minimizing \sum \rho(r_i), where \rho is a residual function of choice.
  *
  * To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:
  *
  * Name        Symbol          Least-Squares   L1-norm    Huber
- * Loss        \rho(x)         0.5*x^2         |x|        0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
+ * Residual    \rho(x)         0.5*x^2         |x|        0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
  * Derivative  \phi(x)         x               sgn(x)     x       if |x|<k, k sgn(x)         otherwise
  * Weight      w(x)=\phi(x)/x  1               1/|x|      1       if |x|<k, k/|x|            otherwise
  *
@@ -75,7 +75,7 @@ class GTSAM_EXPORT Base {
    * the quadratic function for an L2 penalty, the absolute value function for
    * an L1 penalty, etc.
    *
-   * TODO(mikebosse): When the loss function has as input the norm of the
+   * TODO(mikebosse): When the residual function has as input the norm of the
    * residual vector, then it prevents implementations of asymmeric loss
    * functions. It would be better for this function to accept the vector and
    * internally call the norm if necessary.
@@ -85,10 +85,11 @@ class GTSAM_EXPORT Base {
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
   virtual double residual(double distance) const { return loss(distance); };
 #endif
+
   /*
    * This method is responsible for returning the weight function for a given
    * amount of error. The weight function is related to the analytic derivative
-   * of the loss function. See
+   * of the residual function. See
    *  https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
    * for details. This method is required when optimizing cost functions with
    * robust penalties using iteratively re-weighted least squares.
@@ -126,7 +127,7 @@ class GTSAM_EXPORT Base {
   }
 };
 
-/// Null class is the L2 norm and not robust; equivalent to a Gaussian noise model with no loss function.
+/// Null class is not robust so is a Gaussian ?
 class GTSAM_EXPORT Null : public Base {
  public:
   typedef boost::shared_ptr<Null> shared_ptr;
@@ -134,7 +135,7 @@ class GTSAM_EXPORT Null : public Base {
   Null(const ReweightScheme reweight = Block) : Base(reweight) {}
   ~Null() {}
   double weight(double /*error*/) const { return 1.0; }
-  double loss(double error) const { return 0.5 * error * error; }
+  double residual(double error) const { return error; }
   void print(const std::string &s) const;
   bool equals(const Base & /*expected*/, double /*tol*/) const { return true; }
   static shared_ptr Create();
diff --git a/gtsam/linear/tests/testNoiseModel.cpp b/gtsam/linear/tests/testNoiseModel.cpp
index e879731cb..d6b133b98 100644
--- a/gtsam/linear/tests/testNoiseModel.cpp
+++ b/gtsam/linear/tests/testNoiseModel.cpp
@@ -467,10 +467,10 @@ TEST(NoiseModel, robustFunctionFair)
   DOUBLES_EQUAL(0.3333333333333333, fair->weight(error3), 1e-8);
   DOUBLES_EQUAL(0.8333333333333333, fair->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.441961080151135, fair->loss(error1), 1e-8);
-  DOUBLES_EQUAL(22.534692783297260, fair->loss(error2), 1e-8);
-  DOUBLES_EQUAL(22.534692783297260, fair->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.441961080151135, fair->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.441961080151135, fair->residual(error1), 1e-8);
+  DOUBLES_EQUAL(22.534692783297260, fair->residual(error2), 1e-8);
+  DOUBLES_EQUAL(22.534692783297260, fair->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.441961080151135, fair->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionHuber)
@@ -483,10 +483,10 @@ TEST(NoiseModel, robustFunctionHuber)
   DOUBLES_EQUAL(0.5, huber->weight(error3), 1e-8);
   DOUBLES_EQUAL(1.0, huber->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.5000, huber->loss(error1), 1e-8);
-  DOUBLES_EQUAL(37.5000, huber->loss(error2), 1e-8);
-  DOUBLES_EQUAL(37.5000, huber->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.5000, huber->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.5000, huber->residual(error1), 1e-8);
+  DOUBLES_EQUAL(37.5000, huber->residual(error2), 1e-8);
+  DOUBLES_EQUAL(37.5000, huber->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.5000, huber->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionCauchy)
@@ -499,10 +499,10 @@ TEST(NoiseModel, robustFunctionCauchy)
   DOUBLES_EQUAL(0.2000, cauchy->weight(error3), 1e-8);
   DOUBLES_EQUAL(0.961538461538461, cauchy->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.490258914416017, cauchy->loss(error1), 1e-8);
-  DOUBLES_EQUAL(20.117973905426254, cauchy->loss(error2), 1e-8);
-  DOUBLES_EQUAL(20.117973905426254, cauchy->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.490258914416017, cauchy->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.490258914416017, cauchy->residual(error1), 1e-8);
+  DOUBLES_EQUAL(20.117973905426254, cauchy->residual(error2), 1e-8);
+  DOUBLES_EQUAL(20.117973905426254, cauchy->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.490258914416017, cauchy->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionGemanMcClure)
@@ -514,10 +514,10 @@ TEST(NoiseModel, robustFunctionGemanMcClure)
   DOUBLES_EQUAL(9.80296e-5, gmc->weight(error3), 1e-8);
   DOUBLES_EQUAL(0.25      , gmc->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.2500, gmc->loss(error1), 1e-8);
-  DOUBLES_EQUAL(0.495049504950495, gmc->loss(error2), 1e-8);
-  DOUBLES_EQUAL(0.495049504950495, gmc->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.2500, gmc->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.2500, gmc->residual(error1), 1e-8);
+  DOUBLES_EQUAL(0.495049504950495, gmc->residual(error2), 1e-8);
+  DOUBLES_EQUAL(0.495049504950495, gmc->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.2500, gmc->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionWelsch)
@@ -530,10 +530,10 @@ TEST(NoiseModel, robustFunctionWelsch)
   DOUBLES_EQUAL(0.018315638888734, welsch->weight(error3), 1e-8);
   DOUBLES_EQUAL(0.960789439152323, welsch->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.490132010595960, welsch->loss(error1), 1e-8);
-  DOUBLES_EQUAL(12.271054513890823, welsch->loss(error2), 1e-8);
-  DOUBLES_EQUAL(12.271054513890823, welsch->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.490132010595960, welsch->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.490132010595960, welsch->residual(error1), 1e-8);
+  DOUBLES_EQUAL(12.271054513890823, welsch->residual(error2), 1e-8);
+  DOUBLES_EQUAL(12.271054513890823, welsch->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.490132010595960, welsch->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionTukey)
@@ -546,10 +546,10 @@ TEST(NoiseModel, robustFunctionTukey)
   DOUBLES_EQUAL(0.0, tukey->weight(error3), 1e-8);
   DOUBLES_EQUAL(0.9216, tukey->weight(error4), 1e-8);
 
-  DOUBLES_EQUAL(0.480266666666667, tukey->loss(error1), 1e-8);
-  DOUBLES_EQUAL(4.166666666666667, tukey->loss(error2), 1e-8);
-  DOUBLES_EQUAL(4.166666666666667, tukey->loss(error3), 1e-8);
-  DOUBLES_EQUAL(0.480266666666667, tukey->loss(error4), 1e-8);
+  DOUBLES_EQUAL(0.480266666666667, tukey->residual(error1), 1e-8);
+  DOUBLES_EQUAL(4.166666666666667, tukey->residual(error2), 1e-8);
+  DOUBLES_EQUAL(4.166666666666667, tukey->residual(error3), 1e-8);
+  DOUBLES_EQUAL(0.480266666666667, tukey->residual(error4), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionDCS)
@@ -560,8 +560,8 @@ TEST(NoiseModel, robustFunctionDCS)
   DOUBLES_EQUAL(1.0       , dcs->weight(error1), 1e-8);
   DOUBLES_EQUAL(0.00039211, dcs->weight(error2), 1e-8);
 
-  DOUBLES_EQUAL(0.5         , dcs->loss(error1), 1e-8);
-  DOUBLES_EQUAL(0.9900990099, dcs->loss(error2), 1e-8);
+  DOUBLES_EQUAL(0.5         , dcs->residual(error1), 1e-8);
+  DOUBLES_EQUAL(0.9900990099, dcs->residual(error2), 1e-8);
 }
 
 TEST(NoiseModel, robustFunctionL2WithDeadZone)
@@ -576,12 +576,12 @@ TEST(NoiseModel, robustFunctionL2WithDeadZone)
   DOUBLES_EQUAL(0.00990099009, lsdz->weight(e4), 1e-8);
   DOUBLES_EQUAL(0.9,           lsdz->weight(e5), 1e-8);
 
-  DOUBLES_EQUAL(40.5,    lsdz->loss(e0), 1e-8);
-  DOUBLES_EQUAL(0.00005, lsdz->loss(e1), 1e-8);
-  DOUBLES_EQUAL(0.0,     lsdz->loss(e2), 1e-8);
-  DOUBLES_EQUAL(0.0,     lsdz->loss(e3), 1e-8);
-  DOUBLES_EQUAL(0.00005, lsdz->loss(e4), 1e-8);
-  DOUBLES_EQUAL(40.5,    lsdz->loss(e5), 1e-8);
+  DOUBLES_EQUAL(40.5,    lsdz->residual(e0), 1e-8);
+  DOUBLES_EQUAL(0.00005, lsdz->residual(e1), 1e-8);
+  DOUBLES_EQUAL(0.0,     lsdz->residual(e2), 1e-8);
+  DOUBLES_EQUAL(0.0,     lsdz->residual(e3), 1e-8);
+  DOUBLES_EQUAL(0.00005, lsdz->residual(e4), 1e-8);
+  DOUBLES_EQUAL(40.5,    lsdz->residual(e5), 1e-8);
 }
 
 /* ************************************************************************* */
diff --git a/matlab/gtsam_examples/VisualizeMEstimators.m b/matlab/gtsam_examples/VisualizeMEstimators.m
index ce505df5d..8a0485334 100644
--- a/matlab/gtsam_examples/VisualizeMEstimators.m
+++ b/matlab/gtsam_examples/VisualizeMEstimators.m
@@ -48,7 +48,7 @@ function plot_m_estimator(x, model, plot_title, fig_id, filename)
     rho = zeros(size(x));
     for i = 1:size(x, 2)
         w(i) = model.weight(x(i));
-        rho(i) = model.loss(x(i));
+        rho(i) = model.residual(x(i));
     end
 
     psi = w .* x;