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Robust stochastic optimization with the proximal point method
Damek Davis (dsd95 Abstract: Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. In this work, we show that a wide class of such algorithms on strongly convex problems can be augmented with sub-exponential confidence bounds at an overhead cost that is only polylogarithmic in the condition number and the confidence level. We discuss consequences both for streaming and offline algorithms. Keywords: stochastic, proximal point, acceleration, high probability, median of means Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Stochastic Programming Citation: 07/2019 Download: [PDF] Entry Submitted: 07/31/2019 Modify/Update this entry | ||
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