@@ -139,14 +139,14 @@ def add_loss_to_params(learner_type, existing_params):

     return [dict(**existing_params, **lp) for lp in loss_params]

-def run_with(*learner_types):

+def run_with(*learner_types, with_all_loss_functions=True):

     pars = []

     for l in learner_types:

         has_marker = isinstance(l, tuple)

         if has_marker:

             marker, l = l

         for f, k in learner_function_combos[l]:

-            ks = add_loss_to_params(l, k)

+            ks = add_loss_to_params(l, k) if with_all_loss_functions else [k]

             for k in ks:

                 # Check if learner was marked with our `xfail` decorator

                 # XXX: doesn't work when feeding kwargs to xfail.

@@ -402,7 +402,8 @@ def test_learner_performance_is_invariant_under_scaling(learner_type, f, learner

     assert abs(learner.loss() - control.loss()) / learner.loss() < 1e-11

-@run_with(Learner1D, Learner2D, LearnerND, AverageLearner)

+@run_with(Learner1D, Learner2D, LearnerND, AverageLearner,

+    with_all_loss_functions=False)

 def test_balancing_learner(learner_type, f, learner_kwargs):

     """Test if the BalancingLearner works with the different types of learners."""

     learners = [learner_type(generate_random_parametrization(f), **learner_kwargs)

@@ -436,7 +437,8 @@ def test_balancing_learner(learner_type, f, learner_kwargs):

 @run_with(Learner1D, Learner2D, LearnerND, AverageLearner,

-    maybe_skip(SKOptLearner), IntegratorLearner)

+    maybe_skip(SKOptLearner), IntegratorLearner,

+    with_all_loss_functions=False)

 def test_saving(learner_type, f, learner_kwargs):

     f = generate_random_parametrization(f)

     learner = learner_type(f, **learner_kwargs)

@@ -457,7 +459,8 @@ def test_saving(learner_type, f, learner_kwargs):

 @run_with(Learner1D, Learner2D, LearnerND, AverageLearner,

-    maybe_skip(SKOptLearner), IntegratorLearner)

+    maybe_skip(SKOptLearner), IntegratorLearner,

+    with_all_loss_functions=False)

 def test_saving_of_balancing_learner(learner_type, f, learner_kwargs):

     f = generate_random_parametrization(f)

     learner = BalancingLearner([learner_type(f, **learner_kwargs)])

@@ -483,7 +486,8 @@ def test_saving_of_balancing_learner(learner_type, f, learner_kwargs):

 @run_with(Learner1D, Learner2D, LearnerND, AverageLearner,

-    maybe_skip(SKOptLearner), IntegratorLearner)

+    maybe_skip(SKOptLearner), IntegratorLearner,

+    with_all_loss_functions=False)

 def test_saving_with_datasaver(learner_type, f, learner_kwargs):

     f = generate_random_parametrization(f)

     g = lambda x: {'y': f(x), 't': random.random()}