Two zeroth-order methods, including a variance-reduced one-point estimator, are shown to converge to stationary points with sample complexity O(d^9/2 epsilon^-6) for nonconvex decision-dependent stochastic problems.
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Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions
Two zeroth-order methods, including a variance-reduced one-point estimator, are shown to converge to stationary points with sample complexity O(d^9/2 epsilon^-6) for nonconvex decision-dependent stochastic problems.