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Pré-publication, Document de travail

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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Pré-publication, Document de travail
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https://hal.archives-ouvertes.fr/hal-03666898
Contributeur : Florian COMBES Connectez-vous pour contacter le contributeur
Soumis le : jeudi 12 mai 2022 - 19:22:32
Dernière modification le : samedi 14 mai 2022 - 03:40:20

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OptimalSampling.pdf
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  • HAL Id : hal-03666898, version 1

Citation

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

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