Adaptive and robust multi-task learning

We study the multitask learning problem that aims to simultaneously analyze multiple data sets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling th...

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Veröffentlicht in:	The Annals of statistics 2023-10, Vol.51 (5), p.2015
Hauptverfasser:	Duan, Yaqi, Wang, Kaizheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Clustering Datasets Multitasking Outliers (statistics) Problem based learning Robustness (mathematics)
Online-Zugang:	Volltext
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