We consider minimization of stochastic functionals that are compositions of a (potentially) non-smooth convex function h and smooth function c. We develop two stochastic methods--a stochastic prox-linear algorithm and a stochastic (generalized) sub- gradient procedure--and prove that, under mild technical conditions, each converges to stationary points of the stochastic objective. Additionally,...