  Adversarial Optimization (AO) provides a reliable, practical way to match two
implicitly defined distributions, one of which is usually represented by a
sample of real data, and the other is defined by a generator. Typically, AO
involves training of a high-capacity model on each step of the optimization. In
this work, we consider computationally heavy generators, for which training of
high-capacity models is associated with substantial computational costs. To
address this problem, we introduce a novel family of divergences, which varies
the capacity of the underlying model, and allows for a significant acceleration
with respect to the number of samples drawn from the generator. We demonstrate
the performance of the proposed divergences on several tasks, including tuning
parameters of a physics simulator, namely, Pythia event generator.
