diff --git a/src/mlpack/methods/lmnn/lmnn.hpp b/src/mlpack/methods/lmnn/lmnn.hpp
index 0517d5ec51..a62f8b86e4 100644
--- a/src/mlpack/methods/lmnn/lmnn.hpp
+++ b/src/mlpack/methods/lmnn/lmnn.hpp
@@ -79,7 +79,8 @@ class LMNN
    *
    * @param outputMatrix Covariance matrix of Mahalanobis distance.
    */
-  void LearnDistance(arma::mat& outputMatrix);
+  template<typename... CallbackTypes>
+  void LearnDistance(arma::mat& outputMatrix, CallbackTypes&&... callbacks);
 
 
   //! Get the dataset reference.
diff --git a/src/mlpack/methods/lmnn/lmnn_impl.hpp b/src/mlpack/methods/lmnn/lmnn_impl.hpp
index 3f6b539bee..4669e74281 100644
--- a/src/mlpack/methods/lmnn/lmnn_impl.hpp
+++ b/src/mlpack/methods/lmnn/lmnn_impl.hpp
@@ -36,7 +36,9 @@ LMNN<MetricType, OptimizerType>::LMNN(const arma::mat& dataset,
 { /* nothing to do */ }
 
 template<typename MetricType, typename OptimizerType>
-void LMNN<MetricType, OptimizerType>::LearnDistance(arma::mat& outputMatrix)
+template<typename... CallbackTypes>
+void LMNN<MetricType, OptimizerType>::LearnDistance(arma::mat& outputMatrix,
+    CallbackTypes&&... callbacks)
 {
   // LMNN objective function.
   LMNNFunction<MetricType> objFunction(dataset, labels, k,
@@ -56,7 +58,7 @@ void LMNN<MetricType, OptimizerType>::LearnDistance(arma::mat& outputMatrix)
 
   Timer::Start("lmnn_optimization");
 
-  optimizer.Optimize(objFunction, outputMatrix);
+  optimizer.Optimize(objFunction, outputMatrix, callbacks...);
 
   Timer::Stop("lmnn_optimization");
 }