(3), measuring the wall time needed to execute this task.

5. Train a set of supervised learning algorithms (see Table 4 for details): (a) on the real training data set (i.e., the
Oracle); and (b) on the synthetic training datasets generated by FORGE, CTGAN, TVAE, CTAB-GAN+ and RCC-GAN.

6. Evaluate the performance of the learning algorithms from step (5) on the test set.

7. For each dataset, average performance metrics (accuracy, F1-scores) across learners. We report F1-scores for the

positive class, e.g. ‘>50k’ for adult, ‘+50000’ for census and ‘1’ for credit.

Table 4: Benchmark Setup. Supervised learning algorithms for prediction: (A) Adaboost, estimators = 50 , (B) Decision
Tree, tree depth for binary/multiclass target = 15/30, (C) Logistic Regression, (D) MLP, hidden layers for binary/multiclass
target = 50/100
Dataset

Link to dataset

Train/Test

Learner
Adversarial Random Forests for Density Estimation and Generative Modeling