diff --git a/experiments/Manifest.toml b/experiments/Manifest.toml
index f7c37dd6eaff0a738e5a24d25893c715b0b35bb0..9027f359713afa50cb343df2ac2a1746615238a4 100644
--- a/experiments/Manifest.toml
+++ b/experiments/Manifest.toml
@@ -2232,10 +2232,10 @@ version = "1.3.0"
     InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
 
 [[deps.StatsModels]]
-deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Printf", "REPL", "ShiftedArrays", "SparseArrays", "StatsBase", "StatsFuns", "Tables"]
-git-tree-sha1 = "8cc7a5385ecaa420f0b3426f9b0135d0df0638ed"
+deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Printf", "REPL", "ShiftedArrays", "SparseArrays", "StatsAPI", "StatsBase", "StatsFuns", "Tables"]
+git-tree-sha1 = "5cf6c4583533ee38639f73b880f35fc85f2941e0"
 uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
-version = "0.7.2"
+version = "0.7.3"
 
 [[deps.Strided]]
 deps = ["LinearAlgebra", "TupleTools"]
diff --git a/experiments/linearly_separable.jl b/experiments/linearly_separable.jl
index faa3bf278e56aa1d9ef5c3ced6a2dc43b2f361b0..0e54a672a2d3ff25e084f738b777dfd4546c9b22 100644
--- a/experiments/linearly_separable.jl
+++ b/experiments/linearly_separable.jl
@@ -6,6 +6,7 @@ counterfactual_data, test_data = train_test_split(
 run_experiment(
     counterfactual_data, test_data; 
     dataname="Linearly Separable",
-    nsamples=1,
+    nsamples=100,
     nmin=1,
+    niter_eccco=30
 )
\ No newline at end of file
diff --git a/experiments/models/models.jl b/experiments/models/models.jl
index 900e95b9637bbe095e4b1997a93ee70015c50ae0..63c1c4eb07735a3059148fe04355fe5e9d4f5f48 100644
--- a/experiments/models/models.jl
+++ b/experiments/models/models.jl
@@ -47,10 +47,16 @@ function prepare_models(exper::Experiment)
         @info "Training models."
         model_dict = train_models(models, X, labels; parallelizer=exper.parallelizer, train_parallel=exper.train_parallel, cov=exper.coverage)
     else
+        # Pre-trained models:
         if !(is_multi_processed(exper) && MPI.Comm_rank(exper.parallelizer.comm) != 0)
+            # Load models on root process:
             @info "Loading pre-trained models."
             model_dict = Serialization.deserialize(joinpath(pretrained_path(exper), "$(exper.save_name)_models.jls"))
+        else
+            # Dummy model on other processes: 
+            model_dict = nothing
         end
+        # Broadcast models:
         if is_multi_processed(exper)
             model_dict = MPI.bcast(model_dict, exper.parallelizer.comm; root=0)
             MPI.Barrier(exper.parallelizer.comm)