diff --git a/artifacts/results/gmsc_benchmark.csv b/artifacts/results/gmsc_benchmark.csv
index aa40c33137d34f55edd46c9ecb2baa3ea19daf15..6b2a204065be8c7e00803eb658368bd485ba10dc 100644
--- a/artifacts/results/gmsc_benchmark.csv
+++ b/artifacts/results/gmsc_benchmark.csv
@@ -1,1921 +1,1921 @@
 sample,variable,value,dataname,generator,model,target,factual
-1,distance,105.5668716430664,GMSC,Wachter,JEM Ensemble,0,1
-1,distance_from_energy,199.6616973876953,GMSC,Wachter,JEM Ensemble,0,1
-1,distance_from_targets,109.25230407714844,GMSC,Wachter,JEM Ensemble,0,1
+1,distance,120.9355697631836,GMSC,Wachter,JEM Ensemble,0,1
+1,distance_from_energy,116.66268920898438,GMSC,Wachter,JEM Ensemble,0,1
+1,distance_from_targets,124.76456451416016,GMSC,Wachter,JEM Ensemble,0,1
 1,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-1,set_size_penalty,0.1362070375872364,GMSC,Wachter,JEM Ensemble,0,1
+1,set_size_penalty,0.077483517932599,GMSC,Wachter,JEM Ensemble,0,1
 1,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-2,distance,105.49590301513672,GMSC,Wachter,JEM Ensemble,0,1
-2,distance_from_energy,191.6537628173828,GMSC,Wachter,JEM Ensemble,0,1
-2,distance_from_targets,109.5181655883789,GMSC,Wachter,JEM Ensemble,0,1
+2,distance,120.515625,GMSC,Wachter,JEM Ensemble,0,1
+2,distance_from_energy,133.9053955078125,GMSC,Wachter,JEM Ensemble,0,1
+2,distance_from_targets,125.28216552734375,GMSC,Wachter,JEM Ensemble,0,1
 2,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-2,set_size_penalty,0.1472804936118055,GMSC,Wachter,JEM Ensemble,0,1
+2,set_size_penalty,0.07748352678476467,GMSC,Wachter,JEM Ensemble,0,1
 2,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-3,distance,105.89042663574219,GMSC,Wachter,JEM Ensemble,0,1
-3,distance_from_energy,173.0655059814453,GMSC,Wachter,JEM Ensemble,0,1
-3,distance_from_targets,108.87983703613281,GMSC,Wachter,JEM Ensemble,0,1
+3,distance,120.837646484375,GMSC,Wachter,JEM Ensemble,0,1
+3,distance_from_energy,134.5688018798828,GMSC,Wachter,JEM Ensemble,0,1
+3,distance_from_targets,124.26130676269531,GMSC,Wachter,JEM Ensemble,0,1
 3,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-3,set_size_penalty,0.11311508321699781,GMSC,Wachter,JEM Ensemble,0,1
+3,set_size_penalty,0.07748349340979566,GMSC,Wachter,JEM Ensemble,0,1
 3,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-4,distance,105.82109832763672,GMSC,Wachter,JEM Ensemble,0,1
-4,distance_from_energy,125.50739288330078,GMSC,Wachter,JEM Ensemble,0,1
-4,distance_from_targets,108.32563018798828,GMSC,Wachter,JEM Ensemble,0,1
+4,distance,120.8486328125,GMSC,Wachter,JEM Ensemble,0,1
+4,distance_from_energy,178.9120330810547,GMSC,Wachter,JEM Ensemble,0,1
+4,distance_from_targets,123.93437194824219,GMSC,Wachter,JEM Ensemble,0,1
 4,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-4,set_size_penalty,0.12034941092331475,GMSC,Wachter,JEM Ensemble,0,1
+4,set_size_penalty,0.0774834961823323,GMSC,Wachter,JEM Ensemble,0,1
 4,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-5,distance,106.42188262939453,GMSC,Wachter,JEM Ensemble,0,1
-5,distance_from_energy,169.12806701660156,GMSC,Wachter,JEM Ensemble,0,1
-5,distance_from_targets,107.23700714111328,GMSC,Wachter,JEM Ensemble,0,1
+5,distance,120.73995971679688,GMSC,Wachter,JEM Ensemble,0,1
+5,distance_from_energy,106.9113540649414,GMSC,Wachter,JEM Ensemble,0,1
+5,distance_from_targets,123.8321304321289,GMSC,Wachter,JEM Ensemble,0,1
 5,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-5,set_size_penalty,0.13734679027445362,GMSC,Wachter,JEM Ensemble,0,1
+5,set_size_penalty,0.0774835099277329,GMSC,Wachter,JEM Ensemble,0,1
 5,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-6,distance,105.92036437988281,GMSC,Wachter,JEM Ensemble,0,1
-6,distance_from_energy,169.34536743164062,GMSC,Wachter,JEM Ensemble,0,1
-6,distance_from_targets,107.96342468261719,GMSC,Wachter,JEM Ensemble,0,1
+6,distance,120.7047119140625,GMSC,Wachter,JEM Ensemble,0,1
+6,distance_from_energy,125.97723388671875,GMSC,Wachter,JEM Ensemble,0,1
+6,distance_from_targets,123.89594268798828,GMSC,Wachter,JEM Ensemble,0,1
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 6,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-7,distance,105.90818786621094,GMSC,Wachter,JEM Ensemble,0,1
-7,distance_from_energy,184.94619750976562,GMSC,Wachter,JEM Ensemble,0,1
-7,distance_from_targets,108.85952758789062,GMSC,Wachter,JEM Ensemble,0,1
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-8,distance,105.93956756591797,GMSC,Wachter,JEM Ensemble,0,1
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-8,distance_from_targets,108.95744323730469,GMSC,Wachter,JEM Ensemble,0,1
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-8,set_size_penalty,0.11592570274157277,GMSC,Wachter,JEM Ensemble,0,1
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-9,distance,105.75850677490234,GMSC,Wachter,JEM Ensemble,0,1
-9,distance_from_energy,154.72340393066406,GMSC,Wachter,JEM Ensemble,0,1
-9,distance_from_targets,107.80846405029297,GMSC,Wachter,JEM Ensemble,0,1
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+9,distance_from_targets,124.07660675048828,GMSC,Wachter,JEM Ensemble,0,1
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-9,set_size_penalty,0.12076038969007197,GMSC,Wachter,JEM Ensemble,0,1
+9,set_size_penalty,0.07748350521190273,GMSC,Wachter,JEM Ensemble,0,1
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-10,distance,105.70622253417969,GMSC,Wachter,JEM Ensemble,0,1
-10,distance_from_energy,174.47503662109375,GMSC,Wachter,JEM Ensemble,0,1
-10,distance_from_targets,107.2823257446289,GMSC,Wachter,JEM Ensemble,0,1
+10,distance,120.90988159179688,GMSC,Wachter,JEM Ensemble,0,1
+10,distance_from_energy,137.1524658203125,GMSC,Wachter,JEM Ensemble,0,1
+10,distance_from_targets,123.83634185791016,GMSC,Wachter,JEM Ensemble,0,1
 10,redundancy,0.0,GMSC,Wachter,JEM Ensemble,0,1
-10,set_size_penalty,0.1543441391866398,GMSC,Wachter,JEM Ensemble,0,1
+10,set_size_penalty,0.07748359340906008,GMSC,Wachter,JEM Ensemble,0,1
 10,validity,1.0,GMSC,Wachter,JEM Ensemble,0,1
-1,distance,2.094165325164795,GMSC,REVISE,JEM Ensemble,0,1
-1,distance_from_energy,164.5673828125,GMSC,REVISE,JEM Ensemble,0,1
-1,distance_from_targets,4.851550579071045,GMSC,REVISE,JEM Ensemble,0,1
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+1,distance_from_energy,166.26927185058594,GMSC,REVISE,JEM Ensemble,0,1
+1,distance_from_targets,5.747004508972168,GMSC,REVISE,JEM Ensemble,0,1
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+1,set_size_penalty,0.23025500064549886,GMSC,REVISE,JEM Ensemble,0,1
 1,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-2,distance,2.4094643592834473,GMSC,REVISE,JEM Ensemble,0,1
-2,distance_from_energy,247.4547119140625,GMSC,REVISE,JEM Ensemble,0,1
-2,distance_from_targets,5.100176811218262,GMSC,REVISE,JEM Ensemble,0,1
+2,distance,3.9664549827575684,GMSC,REVISE,JEM Ensemble,0,1
+2,distance_from_energy,199.83734130859375,GMSC,REVISE,JEM Ensemble,0,1
+2,distance_from_targets,5.425376892089844,GMSC,REVISE,JEM Ensemble,0,1
 2,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
-2,set_size_penalty,0.12827590688964374,GMSC,REVISE,JEM Ensemble,0,1
+2,set_size_penalty,0.12525923654465165,GMSC,REVISE,JEM Ensemble,0,1
 2,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-3,distance,3.35227108001709,GMSC,REVISE,JEM Ensemble,0,1
-3,distance_from_energy,185.65357971191406,GMSC,REVISE,JEM Ensemble,0,1
-3,distance_from_targets,4.843108654022217,GMSC,REVISE,JEM Ensemble,0,1
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+3,distance_from_targets,4.735615253448486,GMSC,REVISE,JEM Ensemble,0,1
 3,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
-3,set_size_penalty,0.5618452799176383,GMSC,REVISE,JEM Ensemble,0,1
+3,set_size_penalty,0.12995306645573979,GMSC,REVISE,JEM Ensemble,0,1
 3,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-4,distance,2.9281983375549316,GMSC,REVISE,JEM Ensemble,0,1
-4,distance_from_energy,188.00340270996094,GMSC,REVISE,JEM Ensemble,0,1
-4,distance_from_targets,5.057042121887207,GMSC,REVISE,JEM Ensemble,0,1
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+4,distance_from_energy,193.66888427734375,GMSC,REVISE,JEM Ensemble,0,1
+4,distance_from_targets,5.316659927368164,GMSC,REVISE,JEM Ensemble,0,1
 4,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
-4,set_size_penalty,0.6506105632164478,GMSC,REVISE,JEM Ensemble,0,1
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 4,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-5,distance,3.4930672645568848,GMSC,REVISE,JEM Ensemble,0,1
-5,distance_from_energy,201.88702392578125,GMSC,REVISE,JEM Ensemble,0,1
-5,distance_from_targets,5.540608882904053,GMSC,REVISE,JEM Ensemble,0,1
+5,distance,2.3345422744750977,GMSC,REVISE,JEM Ensemble,0,1
+5,distance_from_energy,127.73902130126953,GMSC,REVISE,JEM Ensemble,0,1
+5,distance_from_targets,4.835436820983887,GMSC,REVISE,JEM Ensemble,0,1
 5,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
-5,set_size_penalty,0.16287100395709708,GMSC,REVISE,JEM Ensemble,0,1
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 5,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-6,distance,2.5731427669525146,GMSC,REVISE,JEM Ensemble,0,1
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-6,distance_from_targets,4.463589191436768,GMSC,REVISE,JEM Ensemble,0,1
+6,distance,1.7605600357055664,GMSC,REVISE,JEM Ensemble,0,1
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 6,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
-6,set_size_penalty,0.570092794410213,GMSC,REVISE,JEM Ensemble,0,1
+6,set_size_penalty,0.12658353742407558,GMSC,REVISE,JEM Ensemble,0,1
 6,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
-7,distance,2.281126022338867,GMSC,REVISE,JEM Ensemble,0,1
-7,distance_from_energy,191.06202697753906,GMSC,REVISE,JEM Ensemble,0,1
-7,distance_from_targets,4.983875751495361,GMSC,REVISE,JEM Ensemble,0,1
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+7,distance_from_energy,145.21527099609375,GMSC,REVISE,JEM Ensemble,0,1
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 7,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
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 7,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
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-8,distance_from_targets,4.549928188323975,GMSC,REVISE,JEM Ensemble,0,1
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 8,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
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 8,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
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-9,distance_from_energy,165.27011108398438,GMSC,REVISE,JEM Ensemble,0,1
-9,distance_from_targets,5.0394368171691895,GMSC,REVISE,JEM Ensemble,0,1
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 9,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
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 9,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
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-10,distance_from_targets,5.150926113128662,GMSC,REVISE,JEM Ensemble,0,1
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 10,redundancy,0.0,GMSC,REVISE,JEM Ensemble,0,1
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 10,validity,1.0,GMSC,REVISE,JEM Ensemble,0,1
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 1,redundancy,0.0,GMSC,ECCCo,JEM Ensemble,0,1
-1,set_size_penalty,0.436539612241454,GMSC,ECCCo,JEM Ensemble,0,1
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 1,validity,1.0,GMSC,ECCCo,JEM Ensemble,0,1
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 2,validity,1.0,GMSC,ECCCo,JEM Ensemble,0,1
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+3,distance,20.286090850830078,GMSC,ECCCo,JEM Ensemble,0,1
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 3,redundancy,0.0,GMSC,ECCCo,JEM Ensemble,0,1
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 3,validity,1.0,GMSC,ECCCo,JEM Ensemble,0,1
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-4,distance_from_targets,21.146099090576172,GMSC,ECCCo,JEM Ensemble,0,1
+4,distance,19.707653045654297,GMSC,ECCCo,JEM Ensemble,0,1
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 4,redundancy,0.0,GMSC,ECCCo,JEM Ensemble,0,1
-4,set_size_penalty,0.09866411883297133,GMSC,ECCCo,JEM Ensemble,0,1
+4,set_size_penalty,0.07761348497581855,GMSC,ECCCo,JEM Ensemble,0,1
 4,validity,1.0,GMSC,ECCCo,JEM Ensemble,0,1
-5,distance,15.946751594543457,GMSC,ECCCo,JEM Ensemble,0,1
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 10,validity,1.0,GMSC,ECCCo,JEM Ensemble,1,0
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diff --git a/artifacts/results/gmsc_model_performance.csv b/artifacts/results/gmsc_model_performance.csv
index bbcf6e190ae1f31bc7eed8cc659e0a6993b94b77..0011835c2ee8257f3468709f3f945b599862131d 100644
--- a/artifacts/results/gmsc_model_performance.csv
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@@ -1,5 +1,5 @@
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index 1209831d77f1b31cd6a46752a3b738907433dde9..2f54eabb6d41fd860770079ee25673db12e2a311 100644
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diff --git a/artifacts/results/images/moons_generated_MLP.png b/artifacts/results/images/moons_generated_MLP.png
index 8e7078fce81b1ca81c92ab9b331063c078b5697f..8d970ec4af0fa1d1ee25ba3d74f199bbddb1f8b9 100644
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diff --git a/artifacts/results/mnist_architectures.jls b/artifacts/results/mnist_architectures.jls
index 166c2de99758134ab6d45178a62742dc11b65871..42d72be1de103a6e64215bcec3773b7664de7a96 100644
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diff --git a/artifacts/results/mnist_vae.jls b/artifacts/results/mnist_vae.jls
index 6bd916f99fd0f8046def3082ec0ddda04f70474c..12bead8bc75c668cf76bbee75526f88785b60288 100644
Binary files a/artifacts/results/mnist_vae.jls and b/artifacts/results/mnist_vae.jls differ
diff --git a/artifacts/results/mnist_vae_weak.jls b/artifacts/results/mnist_vae_weak.jls
index 43582e6b78a04ca0144dd911bc4b377335c883cd..c81a673624b1d36493bfe580eda7e0ee3a03de7c 100644
Binary files a/artifacts/results/mnist_vae_weak.jls and b/artifacts/results/mnist_vae_weak.jls differ
diff --git a/notebooks/circles.qmd b/notebooks/circles.qmd
index a71667d1b20319e9ffc51b7f5afb8b7e12b99202..6b6aa0cc894317c6d462f8090e75279e7b073d1c 100644
--- a/notebooks/circles.qmd
+++ b/notebooks/circles.qmd
@@ -7,7 +7,7 @@ eval(setup_notebooks)
 
 ```{julia}
 # Hyper:
-_retrain = true
+_retrain = false
 
 # Data:
 test_size = 0.2
@@ -239,6 +239,20 @@ generator_dict = Dict(
 )
 ```
 
+```{julia}
+generator_params = DataFrame(
+    Dict(
+        :λ1 => λ₁,
+        :λ2 => λ₂,
+        :λ3 => λ₃,
+        :opt => string(typeof(opt)),
+        :eta => opt.eta,
+        :dataname => "Circles",
+    )
+)
+CSV.write(joinpath(params_path, "generator/circles.csv"), generator_params)
+```
+
 ### POC
 
 ```{julia}
diff --git a/notebooks/gmsc.qmd b/notebooks/gmsc.qmd
index 17a7646a465f2f48748d487ddf577080f3411afe..b469a56a3f4149b5aaa89ea1cc2ea8160eb59a0f 100644
--- a/notebooks/gmsc.qmd
+++ b/notebooks/gmsc.qmd
@@ -7,12 +7,11 @@ eval(setup_notebooks)
 
 ```{julia}
 # Hyper:
-_retrain = false
+_retrain = true
 
 # Data:
 test_size = 0.2
-n_obs = Int(50000 / (1.0 - test_size))
-counterfactual_data, test_data = train_test_split(load_gmsc(n_obs); test_size=test_size)
+counterfactual_data, test_data = train_test_split(load_gmsc(); test_size=test_size)
 X, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)
 X = table(permutedims(X))
 labels = counterfactual_data.output_encoder.labels
@@ -160,6 +159,20 @@ generator_dict = Dict(
 )
 ```
 
+```{julia}
+generator_params = DataFrame(
+    Dict(
+        :λ1 => λ₁,
+        :λ2 => λ₂,
+        :λ3 => λ₃,
+        :opt => string(typeof(opt)),
+        :eta => opt.eta,
+        :dataname => "GMSC",
+    )
+)
+CSV.write(joinpath(params_path, "generator/gmsc.csv"), generator_params)
+```
+
 ```{julia}
 # Measures:
 measures = [
diff --git a/notebooks/linearly_separable.qmd b/notebooks/linearly_separable.qmd
index 97c2fab50464383afa96df34d6b69044c305474f..c81c9b1ddd5f6a1ff57cf39a6859ddd68da1568c 100644
--- a/notebooks/linearly_separable.qmd
+++ b/notebooks/linearly_separable.qmd
@@ -7,7 +7,7 @@ eval(setup_notebooks)
 
 ```{julia}
 # Hyper:
-_retrain = true
+_retrain = false
 
 # Data:
 test_size = 0.2
@@ -242,6 +242,20 @@ generator_dict = Dict(
 )
 ```
 
+```{julia}
+generator_params = DataFrame(
+    Dict(
+        :λ1 => λ₁,
+        :λ2 => λ₂,
+        :λ3 => λ₃,
+        :opt => string(typeof(opt)),
+        :eta => opt.eta,
+        :dataname => "Linearly Separable",
+    )
+)
+CSV.write(joinpath(params_path, "generator/linearly_separable.csv"), generator_params)
+```
+
 ### POC
 
 ```{julia}
diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index e721f849cd48e85c9244a3fbcbd58f83c4a4bd6b..78de409ace6d779d7bc3cc0fe2ccca4fabb9af33 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -445,6 +445,7 @@ function plot_mnist(
     rng::Union{Int,AbstractRNG}=Random.GLOBAL_RNG,
     _plot_title::Bool=true,
     show_factual::Bool=false,
+    img_height::Int=180,
     kwargs...,
 )
     Random.seed!(rng)
@@ -468,14 +469,14 @@ function plot_mnist(
     _x = CounterfactualExplanations.counterfactual(ce)
     plt = Plots.plot(
         convert2image(MNIST, reshape(_x,28,28)),
-        axis=nothing, 
+        axis=([], false),
         size=(img_height, img_height),
         title=_title
     )
     if show_factual
         plt_factual = Plots.plot(
             convert2image(MNIST, reshape(x,28,28)),
-            axis=nothing, 
+            axis=([], false),
             size=(img_height, img_height),
             title="Factual"
         )
@@ -487,19 +488,19 @@ end
 ```
 
 ```{julia}
-_regen_all_digits = false
+_regen_all_digits = true
 if _regen_all_digits
-    function plot_all_digits(rng=123;verbose=true,kwargs...)
+    function plot_all_digits(rng=123;verbose=true,img_height=180,kwargs...)
         plts = []
         for i in 0:9
             for j in 0:9
                 @info "Generating counterfactual for $(i) -> $(j)"
-                plt = plot_mnist(i,j;kwargs...,rng=rng)
+                plt = plot_mnist(i,j;kwargs...,rng=rng, img_height=img_height)
                 !verbose || display(plt)
                 plts = [plts..., plt]
             end
         end
-        plt = Plots.plot(plts...; size=(img_height*10,img_height*10), layout=(10,10))
+        plt = Plots.plot(plts...; size=(img_height*10,img_height*10), layout=(10,10), dpi=300)
         return plt
     end
     plt = plot_all_digits(generator=eccco_generator)
@@ -514,13 +515,27 @@ end
 
 # Benchmark generators:
 generator_dict = Dict(
-    "Wachter" => WachterGenerator(λ=Λ[1]),
-    "REVISE" => REVISEGenerator(λ=Λ[1]),
+    "Wachter" => WachterGenerator(λ=Λ[1], opt=eccco_generator.opt),
+    "REVISE" => REVISEGenerator(λ=Λ[1], opt=eccco_generator.opt),
     "Schut" => greedy_generator,
     "ECCCo" => eccco_generator,
 )
 ```
 
+```{julia}
+generator_params = DataFrame(
+    Dict(
+        :λ1 => Λ[1],
+        :λ2 => Λ[2],
+        :λ3 => Λ[3],
+        :opt => string(typeof(eccco_generator.opt)),
+        :eta => eccco_generator.opt.eta,
+        :dataname => "MNIST",
+    )
+)
+CSV.write(joinpath(params_path, "generator/mnist.csv"), generator_params)
+```
+
 ```{julia}
 # Measures:
 measures = [
diff --git a/notebooks/moons.qmd b/notebooks/moons.qmd
index 97bf10fa74d32ae5c6caa76b5fdd089cfa288f3a..263c6382ab5505732943bac0f92bbef33d8b720a 100644
--- a/notebooks/moons.qmd
+++ b/notebooks/moons.qmd
@@ -7,7 +7,7 @@ eval(setup_notebooks)
 
 ```{julia}
 # Hyper:
-_retrain = true
+_retrain = false
 
 # Data:
 test_size = 0.2
@@ -239,6 +239,20 @@ generator_dict = Dict(
 )
 ```
 
+```{julia}
+generator_params = DataFrame(
+    Dict(
+        :λ1 => λ₁,
+        :λ2 => λ₂,
+        :λ3 => λ₃,
+        :opt => string(typeof(opt)),
+        :eta => opt.eta,
+        :dataname => "Moons",
+    )
+)
+CSV.write(joinpath(params_path, "generator/moons.csv"), generator_params)
+```
+
 ### POC
 
 ```{julia}
diff --git a/notebooks/tables.Rmd b/notebooks/tables.Rmd
index 1ffcfe04dc15c9a7256775c29af32819faa80c08..421a3be7d662c9a325e4bd188072eeb65b76907e 100644
--- a/notebooks/tables.Rmd
+++ b/notebooks/tables.Rmd
@@ -277,7 +277,7 @@ col_names <- c(
 )
 algin_cols <- c(rep('l',3),rep('c',ncol(tab_full)-3))
 kbl(
-  tab_full, caption = "All results for all datasets: sample averages +/- one standard deviation over all valid counterfactuals. Best outcomes are highlighted in bold. Asterisks indicate that the given value is more than one (*) or two (**) standard deviations away from the baseline (Wachter). \\label{tab:results-full} \\newline", 
+  tab_full, caption = "All results for all datasets: sample averages +/- one standard deviation over all valid counterfactuals. Best outcomes are highlighted in bold. Asterisks indicate that the given value is more than one (*) or two (**) standard deviations away from the baseline (Wachter). \\label{tab:results-full-valid} \\newline", 
   align = "c", col.names=col_names, booktabs = T, escape=F, 
   format="latex"
 ) %>%
@@ -285,4 +285,57 @@ kbl(
   kable_paper(full_width = F) %>%
   collapse_rows(columns = 1:3, latex_hline = "custom", valign = "top", custom_latex_hline = 1:2) %>%
   save_kable("paper/contents/table_all_valid.tex")
+```
+
+## EBM
+
+```{r}
+files <- list.files("artifacts/params/")
+dt <- lapply(files, function(x) {
+    fread(file.path("artifacts/params/", x))
+})
+dt <- Reduce(function(x,y) {rbind(x,y, fill=TRUE)}, dt)
+setcolorder(
+  dt, 
+  c(
+    "dataname", "n_obs",
+    "n_hidden", "n_layers", "activation", "n_ens",
+    "epochs", "batch_size",
+    "jem_sampling_steps", "sgld_batch_size", "lambda"
+  )
+)
+dt_ebm <- dt[,.(dataname, jem_sampling_steps, sgld_batch_size, lambda)]
+col_names <- c(
+  "Dataset",
+  "SGLD Steps", "Batch Size", "$\\lambda$"
+)
+kbl(
+  dt_ebm, caption = "EBM hyperparemeter choices for our experiments. \\label{tab:ebmparams} \\newline", 
+  align = "r", col.names=col_names, booktabs = T, escape=F, 
+  format="latex"
+) %>%
+  kable_styling(font_size = 8) %>%
+  kable_paper(full_width = F) %>%
+  save_kable("paper/contents/table_ebm_params.tex")
+```
+
+## Experimental setup
+
+```{r}
+dt_exp <- dt[,.(dataname, n_obs, n_hidden, n_layers, activation, n_ens, epochs, batch_size)]
+col_names <- c(
+  "Dataset", "Sample Size",
+  "Hidden Units", "Hidden Layers", "Activation", "Ensemble Size",
+  "Epochs", "Batch Size"
+)
+header <- c(" " = 2, "Network Architecture" = 4, "Training" = 2)
+kbl(
+  dt_exp, caption = "Paremeter choices for our experiments. \\label{tab:params} \\newline", 
+  align = "r", col.names=col_names, booktabs = T, escape=F, 
+  format="latex"
+) %>%
+  kable_styling(latex_options = c("scale_down")) %>%
+  kable_paper(full_width = F) %>%
+  add_header_above(header) %>%
+  save_kable("paper/contents/table_params.tex")
 ```
\ No newline at end of file
diff --git a/paper/contents/table_all_valid.tex b/paper/contents/table_all_valid.tex
index 5d4e466bed79d0d056956aec587c32a05a23cd93..ac0dc81b9ed45471f9772607e3859d197f2a7c0c 100644
--- a/paper/contents/table_all_valid.tex
+++ b/paper/contents/table_all_valid.tex
@@ -1,6 +1,6 @@
 \begin{table}
 
-\caption{All results for all datasets: sample averages +/- one standard deviation over all valid counterfactuals. Best outcomes are highlighted in bold. Asterisks indicate that the given value is more than one (*) or two (**) standard deviations away from the baseline (Wachter). \label{tab:results-full} \newline}
+\caption{All results for all datasets: sample averages +/- one standard deviation over all valid counterfactuals. Best outcomes are highlighted in bold. Asterisks indicate that the given value is more than one (*) or two (**) standard deviations away from the baseline (Wachter). \label{tab:results-full-valid} \newline}
 \centering
 \resizebox{\linewidth}{!}{
 \begin{tabular}[t]{ccccccccc}
diff --git a/paper/contents/table_ebm_params.tex b/paper/contents/table_ebm_params.tex
new file mode 100644
index 0000000000000000000000000000000000000000..2136f26e00905f1caebd372ee4e71d5d0b7b919f
--- /dev/null
+++ b/paper/contents/table_ebm_params.tex
@@ -0,0 +1,17 @@
+\begin{table}
+
+\caption{EBM hyperparemeter choices for our experiments. \label{tab:ebmparams} \newline}
+\centering
+\fontsize{8}{10}\selectfont
+\begin{tabular}[t]{rrrr}
+\toprule
+Dataset & SGLD Steps & Batch Size & $\lambda$\\
+\midrule
+Circles & 20 & 100 & 0.01\\
+GMSC & 30 & 10 & 0.10\\
+Linearly Separable & 30 & 50 & 0.10\\
+MNIST & 25 & 10 & 0.01\\
+Moons & 30 & 10 & 0.10\\
+\bottomrule
+\end{tabular}
+\end{table}
diff --git a/paper/contents/table_params.tex b/paper/contents/table_params.tex
new file mode 100644
index 0000000000000000000000000000000000000000..47ba04a01a622806ff6b24f7b2b21599f9482248
--- /dev/null
+++ b/paper/contents/table_params.tex
@@ -0,0 +1,19 @@
+\begin{table}
+
+\caption{Paremeter choices for our experiments. \label{tab:params} \newline}
+\centering
+\resizebox{\linewidth}{!}{
+\begin{tabular}[t]{rrrrrrrr}
+\toprule
+\multicolumn{2}{c}{ } & \multicolumn{4}{c}{Network Architecture} & \multicolumn{2}{c}{Training} \\
+\cmidrule(l{3pt}r{3pt}){3-6} \cmidrule(l{3pt}r{3pt}){7-8}
+Dataset & Sample Size & Hidden Units & Hidden Layers & Activation & Ensemble Size & Epochs & Batch Size\\
+\midrule
+Circles & 1000 & 32 & 3 & swish & 5 & 100 & 100\\
+GMSC & 50000 & 128 & 2 & swish & 5 & 100 & 250\\
+Linearly Separable & 1000 & 16 & 3 & swish & 5 & 100 & 100\\
+MNIST & 10000 & 128 & 1 & swish & 5 & 100 & 128\\
+Moons & 2500 & 32 & 3 & relu & 5 & 500 & 128\\
+\bottomrule
+\end{tabular}}
+\end{table}
diff --git a/paper/paper.pdf b/paper/paper.pdf
index 06118315a41465bc9f4051211ab1cb3aa0088fe1..39a4a39ea354e1e07792b09e7bb43d7ab301d8af 100644
Binary files a/paper/paper.pdf and b/paper/paper.pdf differ
diff --git a/paper/paper.tex b/paper/paper.tex
index 909827ce8ebb8bc5427706dcf1aa56d204d53293..9664fd5ec904b740590fcf84911f291c8937b329 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -361,7 +361,7 @@ As mentioned in the body of the paper, we rely on a biased sampler involving sep
   \end{aligned}
 \end{equation}
 
-Consistent with~\citet{grathwohl2020your}, we have specified $\epsilon=2$ and $\sigma=0.01$ as the default values for all of our experiments. The number of total SGLD steps $J$ varies by dataset. Following best practices, we initialize $\mathbf{x}_0$ randomly in 5\% of all cases and sample from a buffer in all other cases. The buffer itself is randomly initialised and gradually grows to a maximum of 10,000 samples during training as $\hat{\mathbf{x}}_{J}$ is stored in each epoch~\citep{du2020implicit,grathwohl2020your}. 
+Consistent with~\citet{grathwohl2020your}, we have specified $\epsilon=2$ and $\sigma=0.01$ as the default values for all of our experiments. The number of total SGLD steps $J$ varies by dataset (Table~\ref{tab:ebmparams}). Following best practices, we initialize $\mathbf{x}_0$ randomly in 5\% of all cases and sample from a buffer in all other cases. The buffer itself is randomly initialised and gradually grows to a maximum of 10,000 samples during training as $\hat{\mathbf{x}}_{J}$ is stored in each epoch~\citep{du2020implicit,grathwohl2020your}. 
 
 It is important to realise that sampling is done during each training epoch, which makes training Joint Energy Models significantly harder than conventional neural classifiers. In each epoch the generated (batch of) sample(s) $\hat{\mathbf{x}}_{J}$ is used as part of the generative loss component, which compares its energy to that of observed samples $\mathbf{x}$: $L_{\text{gen}}(\theta)=\mu_{\theta}(\mathbf{x})[\mathbf{y}]-\mu_{\theta}(\hat{\mathbf{x}}_{J})[\mathbf{y}]$. Our full training objective can be summarized as follows,
 
@@ -371,9 +371,11 @@ It is important to realise that sampling is done during each training epoch, whi
   \end{aligned}
 \end{equation}
 
-where $L_{\text{reg}}(\theta)$ is a Ridge penalty (L2 norm) that regularises energy magnitudes for both observed and generated samples~\citep{du2020implicit}. We have used varying degrees of regularization depending on the dataset. 
+where $L_{\text{reg}}(\theta)$ is a Ridge penalty (L2 norm) that regularises energy magnitudes for both observed and generated samples~\citep{du2020implicit}. We have used varying degrees of regularization depending on the dataset ($\lambda$ in Table~\ref{tab:ebmparams}). 
 
-Contrary to existing work, we have not typically used the entire minibatch of training data for the generative loss component but found that using a subset of the minibatch was often sufficient in attaining decent generative performance. This has helped to reduce the computational burden for our models, which should make it easier for others to reproduce our findings. Figures~\ref{fig:mnist-gen} and~\ref{fig:moons-gen} show generated samples for our \textit{MNIST} and \textit{Moons} data, to provide a sense of their generative property.
+Contrary to existing work, we have not typically used the entire minibatch of training data for the generative loss component but found that using a subset of the minibatch was often sufficient in attaining decent generative performance (Table~\ref{tab:ebmparams}). This has helped to reduce the computational burden for our models, which should make it easier for others to reproduce our findings. Figures~\ref{fig:mnist-gen} and~\ref{fig:moons-gen} show generated samples for our \textit{MNIST} and \textit{Moons} data, to provide a sense of their generative property.
+
+\import{contents/}{table_ebm_params.tex}
 
 \begin{figure}
   \centering
@@ -386,7 +388,6 @@ Contrary to existing work, we have not typically used the entire minibatch of tr
   \includegraphics[width=0.5\textwidth]{../artifacts/results/images/moons_generated_JEM.png}
   \caption{Conditionally generated samples (stars) for our \textit{Moons} data using a JEM.}\label{fig:moons-gen}
 \end{figure}
-
 \subsubsection{Inference: Quantifying Models' Generative Property}
 
 At inference time, we assume no prior knowledge about the model's generative property. This means that we do not tab into the existing buffer of generated samples for our Joint Energy Models, but instead generate conditional samples from scratch. While have relied on the default values $\epsilon=2$ and $\sigma=0.01$ also during inference, the number of total SGLD steps was set to $J=500$ in all cases, so significantly higher than during training. For all of our synthetic datasets and models, we generated 50 conditional samples and then formed subsets containing the $n_{E}=25$ lowest-energy samples. While in practice it would be sufficient to do this once for each model and dataset, we have chosen to perform sampling separately for each individual counterfactual in our experiment to account for stochasticity. To help reduce the computational burden for our real-world datasets we have generated only 10 conditional samples each time and used all of them in our counterfactual search. Using more samples, as we originally did, had no substantial impact on our results.
@@ -438,9 +439,51 @@ The core part of our code base is integrated into a larger ecosystem of \texttt{
 
 \subsection{Experimental Setup}\label{app:setup}
 
+Table~\ref{tab:params} provides an overview of all parameters related to our experiments. The \textit{GMSC} data were randomly undersampled for balancing purposes and all features were standardized. \textit{MNIST} data was also randomly undersampled for reasons outlined below. Pixel values were preprocessed to fall in the range of $[-1,1]$ and a small Gaussian noise component ($\sigma=0.03$) was added to training samples following common practices in the EBM literature. 
+
+\subsubsection{Compute}
+
+To enable others to easily replicate our experiments, we have chosen to work with small neural network architectures and randomly undersampled large datasets where appropriate. All of our experiments could then be run locally on a personal machine. The longest runtimes we experienced for model training and counterfactual benchmarking were on the order of 8-12 hours (\textit{MNIST} data). For the synthetic data, all experiments could be completed in less than an hour. 
+
+We have summarised the system information below:
+
+\textbf{Software}:
+
+\begin{itemize}
+  \item System Version: macOS 13.3.1
+  \item Kernel Version: Darwin 22.4.0
+  \item Boot Volume: Macintosh HD
+  \item Boot Mode: Normal
+\end{itemize}
+
+\textbf{Hardware}:
 
+\begin{itemize}
+  \item Model Name: MacBook Pro
+  \item Model Identifier: MacBookPro16,1
+  \item Processor Name: 8-Core Intel Core i9
+  \item Processor Speed: 2.3 GHz
+  \item Number of Processors: 1
+  \item Total Number of Cores: 8
+  \item L2 Cache (per Core): 256 KB
+  \item L3 Cache: 16 MB
+  \item Hyper-Threading Technology: Enabled
+  \item Memory: 32 GB
+\end{itemize}
+
+\import{contents/}{table_params.tex}
 \subsection{Results}\label{app:results}
 
+Figure~\ref{fig:mnist-eccco} shows examples of counterfactuals for \textit{MNIST} data where the underlying model is our \textit{JEM Ensemble}. Original images are shown on the diagonal and the corresponding counterfactuals are plotted across rows.
+
+\begin{figure}
+  \centering
+  \includegraphics[width=0.75\textwidth]{../artifacts/results/images/mnist_eccco_all_digits.png}
+  \caption{Counterfactuals for \textit{MNIST} data and our \textit{JEM Ensemble}. Original images are shown on the diagonal with the corresponding counterfactuals plotted across rows.}\label{fig:mnist-eccco}
+\end{figure}
+
+Table~\ref{tab:results-full} reports all of the evaluation metrics we have computed. Table~\ref{tab:results-full} reports the same metric for the subset of valid counterfactuals. The `Unfaithfulness' and `Implausibility' metrics have been discussed extensively in the body of the paper. The `Cost' metric relates to the distance between the factual and the counterfactual. The `Redundancy' metric measures sparsity as the percentage of features that remain unperturbed (higher is better). The `Uncertainty' metric is just the average value of the smooth set size penalty (Equation~\ref{eq:setsize}). Finally, `Validity' is the percentage of valid counterfactuals. 
+
 \import{contents/}{table_all.tex}
 
 \import{contents/}{table_all_valid.tex}