Subsampling under distributional constraints - Ecole Centrale de Marseille Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Subsampling under distributional constraints

Résumé

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.
Fichier principal
Vignette du fichier
OptimalSampling.pdf (446.79 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03666898 , version 1 (12-05-2022)

Identifiants

  • HAL Id : hal-03666898 , version 1

Citer

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

Partager

Gmail Facebook X LinkedIn More