Towards Interpretable Multi-Task Learning Using Bilevel Programming

Understand

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.