  When a number of similar tasks have to be learned simultaneously, multi-task
learning (MTL) models can attain significantly higher accuracy than single-task
learning (STL) models. However, the advantage of MTL depends on various
factors, such as the similarity of the tasks, the sizes of the datasets, and so
on; in fact, some tasks might not benefit from MTL and may even incur a loss of
accuracy compared to STL. Hence, the question arises: which tasks should be
learned together? Domain experts can attempt to group tasks together following
intuition, experience, and best practices, but manual grouping can be
labor-intensive and far from optimal. In this paper, we propose a novel
automated approach for task grouping. First, we study the affinity of tasks for
MTL using four benchmark datasets that have been used extensively in the MTL
literature, focusing on neural network-based MTL models. We identify inherent
task features and STL characteristics that can help us to predict whether a
group of tasks should be learned together using MTL or if they should be
learned independently using STL. Building on this predictor, we introduce a
randomized search algorithm, which employs the predictor to minimize the number
of MTL trainings performed during the search for task groups. We demonstrate on
the four benchmark datasets that our predictor-driven search approach can find
better task groupings than existing baseline approaches.
