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Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis
Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the conflict between classical exponential bounds and observed power-law learning curves. In this work, we extend this result to the stochastic regime
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