  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 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.
