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Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
This paper develops a new dimension-free Azuma-Hoeffding type bound on summation norm of a martingale difference sequence with random individual bounds.
Online Bootstrap Inference with Nonconvex Stochastic Gradient Descent Estimator
Furthermore, our analysis yields an intermediate result: the in-expectation error convergence rate for the original SGD estimator in nonconvex settings, which is comparable to existing results for convex problems.
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