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Better Mini-Batch Algorithms via Accelerated Gradient Methods
Mini-batch algorithms have been proposed as a way to speed-up stochastic
convex optimization problems. We study how such algorithms can be improved
using accelerated gradient methods. We provide a novel analysis, which shows
how standard gradient methods may sometimes be insufficient to obtain a
significant speed-up and propose a novel accelerated gradient algorithm, which
deals with this deficiency, enjoys a uniformly superior guarantee and works
well in practice
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