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Sample Average Approximation for Distributionally Robust Optimization with $ϕ$-divergences
It is well known that estimating the expectation of any given bounded random variable with values in $[-B, B]$ has two basic properties: (1) the number of samples needed scales at the order of $O(B^2/ε^2)$, where $ε$ is the prescribed target precision, and (2) such a sample complexity is independent
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