  We present a new, stochastic variant of the projective splitting (PS) family
of algorithms for monotone inclusion problems. It can solve min-max and
noncooperative game formulations arising in applications such as robust ML
without the convergence issues associated with gradient descent-ascent, the
current de facto standard approach in such situations. Our proposal is the
first version of PS able to use stochastic (as opposed to deterministic)
gradient oracles. It is also the first stochastic method that can solve min-max
games while easily handling multiple constraints and nonsmooth regularizers via
projection and proximal operators. We close with numerical experiments on a
distributionally robust sparse logistic regression problem.
