arXiv stat.ML
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Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
arXiv:2511.02258v3 Announce Type: replace Abstract: This paper studies the high-dimensional scaling limits of online stochastic gradient descent (SGD). Building on the work of Ben Arous, Gheissari, and Jagannath on the effective dynamics of SGD, we study the critical scaling regime of the step size for single-layer net