Finite Sample and Large Deviations Analysis of Stochastic Gradient Algorithm with Correlated Noise

Abstract

We analyze the finite sample regret of a decreasing step size stochastic gradient algorithm. We assume correlated noise and use a perturbed Lyapunov function as a systematic approach for the analysis. Finally we analyze the escape time of the iterates using large deviations theory.