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Subsampling under distributional constraints

Abstract : Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting we have an input X in a general space, and an output Y = f (X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S 1 and S 2 of different sizes such that only f (S 1) is available. We tackle the problem of selecting a subsample S 3 ∈ S 2 of smaller size on which to run the complex model f , and such that distribution of f (S 3) is close to that of f (S 1). We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self-noise data set.
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Preprints, Working Papers, ...
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Contributor : Florian Combes Connect in order to contact the contributor
Submitted on : Thursday, May 12, 2022 - 7:22:32 PM
Last modification on : Saturday, May 14, 2022 - 3:40:20 AM


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  • HAL Id : hal-03666898, version 1


Florian Combes, Ricardo Fraiman, Badih Ghattas. Subsampling under distributional constraints. 2022. ⟨hal-03666898⟩



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