Establishes a central limit theorem for averaged Adam with n^{-1/2} convergence rate to an attracting zero and covariance determined by the algorithm at the attractor.
On Constructing Confidence Region for Model Parameters in Stochas- tic Gradient Descent via Batch Means.arXiv:1911.01483, 2020
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Central limit theorem for the averaged Adam optimizer
Establishes a central limit theorem for averaged Adam with n^{-1/2} convergence rate to an attracting zero and covariance determined by the algorithm at the attractor.