  Variational inequalities are a broad formalism that encompasses a vast number
of applications. Motivated by applications in machine learning and beyond,
stochastic methods are of great importance. In this paper we consider the
problem of stochastic finite-sum cocoercive variational inequalities. For this
class of problems, we investigate the convergence of the method based on the
SARAH variance reduction technique. We show that for strongly monotone problems
it is possible to achieve linear convergence to a solution using this method.
Experiments confirm the importance and practical applicability of our approach.
