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We present a new, stochastic variant of the projective splitting (PS) familyof algorithms for monotone inclusion problems. It can solve min-max andnoncooperative game formulations arising in applications such as robust MLwithout the convergence issues associated with gradient descent-ascent, thecurrent de facto standard approach in such situations. Our proposal is thefirst version of PS able to use stochastic (as opposed to deterministic)gradient oracles. It is also the first stochastic method that can solve min-maxgames while easily handling multiple constraints and nonsmooth regularizers viaprojection and proximal operators. We close with numerical experiments on adistributionally robust sparse logistic regression problem.

Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers