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• W2912881338 abstract "Importance sampling has been extensively studied as a variance reduction tool in rare-event simulation. The design and efficiency of this method often relies on structural knowledge about the target problem in hand. In this paper, we consider a simple rare-event setting, driven by Gaussian input variates, where there is no direct information about the system of interest, or where the system of interest can only be evaluated through a black-box model. This setting mimics, on a basic level, situations where the system of interest could be too complex to analyze as in the conventional importance sampling literature. We investigate an approach based on two-stage sampling, where the first stage learns the rare-event set of interest, and the second stage uses a change of measure that exploits the first-stage finding. We present some guarantees and discuss comparisons of this scheme with more naive approaches." @default.
• W2912881338 created "2019-02-21" @default.
• W2912881338 creator A5007087808 @default.
• W2912881338 creator A5037644321 @default.
• W2912881338 creator A5071064725 @default.
• W2912881338 date "2018-12-09" @default.
• W2912881338 modified "2023-09-26" @default.
• W2912881338 title "Rare-event simulation without structural information: a learning-based approach" @default.
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• W2912881338 doi "https://doi.org/10.5555/3320516.3320737" @default.
• W2912881338 hasPublicationYear "2018" @default.
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