  In this paper, we frame homogeneous-feature multi-task learning (MTL) as a
hierarchical representation learning problem, with one task-agnostic and
multiple task-specific latent representations. Drawing inspiration from the
information bottleneck principle and assuming an additive independent noise
model between the task-agnostic and task-specific latent representations, we
limit the information contained in each task-specific representation. It is
shown that our resulting representations yield competitive performance for
several MTL benchmarks. Furthermore, for certain setups, we show that the
trained parameters of the additive noise model are closely related to the
similarity of different tasks. This indicates that our approach yields a
task-agnostic representation that is disentangled in the sense that its
individual dimensions may be interpretable from a task-specific perspective.
