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A General Framework of Optimal Stochastic Optimization with Dependent Data: Multiple Optimality Guarantee, Sample Complexity, and Tractability
DownloadSpring 2023
We consider stochastic optimization in settings where the distribution of unknown parameters is observable only through finitely dependent training samples. Using the Sample Averaging Approximation ({SAA}), we specifically study the data-driven procedure in which, instead of receiving samples...
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