  We propose stochastic variance reduced algorithms for solving convex-concave
saddle point problems, monotone variational inequalities, and monotone
inclusions. Our framework applies to extragradient, forward-backward-forward,
and forward-reflected-backward methods both in Euclidean and Bregman setups.
All proposed methods converge in exactly the same setting as their
deterministic counterparts and they either match or improve the best-known
complexities for solving structured min-max problems. Our results reinforce the
correspondence between variance reduction in variational inequalities and
minimization. We also illustrate the improvements of our approach with
numerical evaluations on matrix games.
