erator” is a simple sampling scheme that draws from the
marginals in adaptively selected subregions; the “discrimi-
nator” is a RF classiﬁer. The result can be understood as a
zero-sum game in which adversaries take turns increasing
and decreasing label uncertainty at each round. However,
beyond this conceptual link between our method and GANs
lie some important differences. Both generator and discrim-
inator share the same parameters in our algorithm. Indeed,
our generator does not, strictly speaking, learn anything; it
merely exploits what the discriminator has learned. This
means that ARFs cannot be used for adversarial attacks of
the sort made famous by GANs, which involve separately
parametrized networks for each model. Moreover, the syn-
thetic data generated by ARFs is relatively naïve, consisting
of bootstrap samples drawn from subsets of the original ob-
servations. That is because our goal is not (yet) to generate
new data, but merely to learn an independence-inducing
Adversarial Random Forests for Density Estimation and Generative Modeling