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- W4367305935 abstract "We develop and analyze a dynamic finite-budget allocation scheme for stratified sampling for general purposes on the unit hypercube with adaptive variance reduction applied by strata. By batching the parallelized tasks, the proposed scheme succeeds to update the budget allocation only occasionally yet effectively takes into account the decreasing stratum variances while simultaneously estimating the stratum means and updating the variance reduction parameters throughout. The scheme is designed to accommodate a variety of existing algorithms for the parameter search procedure. In particular, when stochastic approximation is employed, we derive optimal stratum batchwise learning rates in accordance with the adjusted batch size along the way. Numerical results are provided to support the theoretical findings and to illustrate the effectiveness of the proposed scheme." @default.
- W4367305935 created "2023-04-29" @default.
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- W4367305935 date "2023-04-28" @default.
- W4367305935 modified "2023-10-04" @default.
- W4367305935 title "Dynamic Finite-Budget Allocation of Stratified Sampling with Adaptive Variance Reduction by Strata" @default.
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- W4367305935 doi "https://doi.org/10.1137/21m1430996" @default.
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