Related papers: Towards Interpretable Multi-Task Learning Using Bilevel Programming

Towards Interpretable Multi-Task Learning Using Bilevel Programming

URL: http://arxiv.org/abs/2009.05483v1
Date: Fri, 11 Sep 2020 15:04:27 GMT
Title: Towards Interpretable Multi-Task Learning Using Bilevel Programming
Authors: Francesco Alesiani, Shujian Yu, Ammar Shaker and Wenzhe Yin
Abstract summary: 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. 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.
Score: 18.293397644865454
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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

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