Establishes high-probability bounds for zeroth-order GD showing logarithmic dependence on failure probability δ in deterministic case and specific query complexity in stochastic case.
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2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.
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High-Probability Guarantees for Random Zeroth-Order (Stochastic) Gradient Descent
Establishes high-probability bounds for zeroth-order GD showing logarithmic dependence on failure probability δ in deterministic case and specific query complexity in stochastic case.
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Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization
AdaGrad-Norm last iterate achieves O(1/N^{1/4}) suboptimality for convex non-smooth problems, with tight lower bounds.