Multitask Learning Can Improve Worst-Group Outcomes

arXiv:2312.03151v2 Announce Type: replace
Abstract: In order to create machine learning systems that serve a variety of users well, it is vital to not only achieve high average performance but also ensure equitable outcomes across diverse groups. However, most machine learning methods are designed to improve a model's average performance on a chosen end task without consideration for their impact on worst group error. Multitask learning (MTL) is one such widely used technique. In this paper, we seek not only …

arxiv cs.lg multitask learning type