Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)

NeurIPS 2013 Francis BachEric Moulines

We consider the stochastic approximation problem where a convex function has to be minimized, given only the knowledge of unbiased estimates of its gradients at certain points, a framework which includes machine learning methods based on the minimization of the empirical risk. We focus on problems without strong convexity, for which all previously known algorithms achieve a convergence rate for function values of O(1/n^{1/2})... (read more)

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