  Interpretable Multi-Task Learning can be expressed as learning a sparse graph
of the task relationship based on the prediction performance of the learned
models. Since many natural phenomenon exhibit sparse structures, enforcing
sparsity on learned models reveals the underlying task relationship. Moreover,
different sparsification degrees from a fully connected graph uncover various
types of structures, like cliques, trees, lines, clusters or fully disconnected
graphs. In this paper, we propose a bilevel formulation of multi-task learning
that induces sparse graphs, thus, revealing the underlying task relationships,
and an efficient method for its computation. We show empirically how the
induced sparse graph improves the interpretability of the learned models and
their relationship on synthetic and real data, without sacrificing
generalization performance. Code at https://bit.ly/GraphGuidedMTL
