A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
Paper recorded by Signals 4 on 2026-10-13 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14
Category: cs.LG · 机器学习 · first seen 2026-10-14
Abstract
We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bounds for stochastic gradient algorithms. Under generic moment and smoothness conditions, the convergence analysis of the stochastic gradient method reduces to a calculus argument verifying a Lyapunov generator inequality for the SDE.
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Cite this page: A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus: the #296 most recent of 749 cs.LG papers we have recorded (as of 2026-10-13). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-rate-transfer-method-convergence-rates-in-stochastic-gradient-descent-via-stoc.html
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