Autor:	B��hlmann, Peter, ��evid, Domagoj
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Methodology (stat.ME) FOS: Computer and information sciences 62J07 Statistics - Machine Learning Machine Learning (stat.ML) Statistics - Methodology
Popis:	We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts to the issue on concept drift, raised by Efron (2020), when the data generating distribution is changing. 23 pages, 7 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95f3ee45f3dfad13999d6ef0def8ff3b http://arxiv.org/abs/2008.06234 Zobrazit plný text záznamu