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Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis

arXiv:2609.36271v1 Announce Type: cross Abstract: 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 wo…

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  1. 2026-09-30 04:00 · arXiv stat.ML
    Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis

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