Weakly Supervised Multi-output Regression via Correlated Gaussian Processes

Seokhyun Chung (), Raed Al Kontar () and Zhenke Wu ()
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Seokhyun Chung: Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Raed Al Kontar: Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Zhenke Wu: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109

INFORMS Joural on Data Science, 2022, vol. 1, issue 2, 115-137

Abstract: Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental assumption is that the output/group membership labels for all observations are known. This assumption is often violated in real applications. For instance, in healthcare data sets, sensitive attributes such as ethnicity are often missing or unreported. To this end, we introduce a weakly supervised multi-output model based on dependent Gaussian processes. Our approach is able to leverage data without complete group labels or possibly only prior belief on group memberships to enhance accuracy across all outputs. Through intensive simulations and case studies on insulin, testosterone and body fat data sets, we show that our model excels in multi-output settings with missing labels while being competitive in traditional fully labeled settings. We end by highlighting the possible use of our approach in fair inference and sequential decision making.

Keywords: weakly supervised learning; multi-output regression; Gaussian process; real-life applications (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:1:y:2022:i:2:p:115-137

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