Anytime Online-to-Batch Conversions, Optimism, and Acceleration

3 Mar 2019 Ashok Cutkosky

A standard way to obtain convergence guarantees in stochastic convex optimization is to run an online learning algorithm and then output the average of its iterates: the actual iterates of the online learning algorithm do not come with individual guarantees. We close this gap by introducing a black-box modification to any online learning algorithm whose iterates converge to the optimum in stochastic scenarios... (read more)

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