Distributional anchor regression

Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating...

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Detalles Bibliográficos
Autores principales: Kook, Lucas, Sick, Beate, Bühlmann, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106647/
https://www.ncbi.nlm.nih.gov/pubmed/35582000
http://dx.doi.org/10.1007/s11222-022-10097-z

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