Adaptive and Robust Multi-Task Learning

• URL: http://arxiv.org/abs/2202.05250v4
• Date: Sat, 16 Sep 2023 22:24:06 GMT
• Title: Adaptive and Robust Multi-Task Learning
• Authors: Yaqi Duan, Kaizheng Wang
• Abstract summary: We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks. We derive sharp statistical guarantees for the methods and prove their robustness against outlier tasks.
• Score: 8.883733362171036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences. We derive sharp statistical guarantees for the methods and prove their robustness against outlier tasks. Numerical experiments on synthetic and real datasets demonstrate the efficacy of our new methods.

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