Self-Paced Multi-Task Learning

作者：Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2017 · DOI：10.1609/aaai.v31i1.10847 · 被引用次数：79 · 研究领域：Domain Adaptation and Few-Shot Learning、Machine Learning and ELM、Sparse and Compressive Sensing Techniques

Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that attempts to learn the tasks by simultaneously taking into consideration the complexities of both tasks and instances per task. We propose a novel formulation by presenting a new task-oriented regularizer that can jointly prioritize tasks and instances.Thus it can be interpreted as a self-paced learner for multi-task learning. An efficient block coordinate descent algorithm is developed to solve the proposed objective function, and the convergence of the algorithm can be guaranteed. Experimental results on the toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-arts.