  This paper provides a unifying view of a wide range of problems of interest
in machine learning by framing them as the minimization of functionals defined
on the space of probability measures. In particular, we show that generative
adversarial networks, variational inference, and actor-critic methods in
reinforcement learning can all be seen through the lens of our framework. We
then discuss a generic optimization algorithm for our formulation, called
probability functional descent (PFD), and show how this algorithm recovers
existing methods developed independently in the settings mentioned earlier.
