Abstract
Task Relation Discovery (TRD), i.e., reveal the relation of tasks, has notable value: it is the key
concept underlying Multi-task Learning (MTL) and
provides a principled way for identifying redundancies across tasks. However, task relation is usually
specifically determined by data scientist resulting
in the additional human effort for TRD, while transfer based on brute-force methods or mere training samples may cause negative effects which degrade the learning performance. To avoid negative transfer in an automatic manner, our idea is to
leverage commonly available context attributes in
nowadays systems, i.e., the metadata. In this paper, we, for the first time, introduce metadata into
TRD for MTL and propose a novel Metadata Clustering method, which jointly uses historical samples and additional metadata to automatically exploit the true relatedness. It also avoids the negative transfer by identifying reusable samples between related tasks. Experimental results on five
real-world datasets demonstrate that the proposed
method is effective for MTL with TRD, and particularly useful in complicated systems with diverse
metadata but insufficient data samples