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.
Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method.arXiv:2603.18899, 2026
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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.