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Sharp High-Probability Statistical Rates for Heavy-Tailed Nonsmooth Convex Optimization
We establish sharp high-probability statistical rates for nonsmooth stochastic convex optimization under heavy-tailed noise, closing the confidence-dependence gap in both general convex and strongly convex settings. We consider composite objectives with a Lipschitz convex loss and a known proximal r
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