Treating stochastic and deterministic gradients separately in mini-batch SGD yields faster convergence and smaller error radius than uniform treatment, with further gains under strong convexity.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization I: A generic algorithmic framework,
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Stochastic versus Deterministic in Stochastic Gradient Descent
Treating stochastic and deterministic gradients separately in mini-batch SGD yields faster convergence and smaller error radius than uniform treatment, with further gains under strong convexity.