Confidence Weighted Multitask Learning
By Peng Yang, Peilin Zhao, Jiayu Zhou, Xin Gao. 2019
7 citations recorded by the source corpus.
Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process. The mean (covariance) of the Gaussian Distribution is a sum of a local component and a global component that is shared among all the tasks. In addition, this paper also addresses the challenge of active learning on the online multitask setting. Instead of requiring labels of all the instances, the proposed algorithm determines whether the learner should acquire a label by considering the confidence from its related tasks over label prediction. Theoretical results show the regret bounds can be significantly reduced. Empirical results demonstrate that the proposed algorithm is able to achieve promising learning efficacy, while simultaneously minimizing the labeling cost.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Qiang Yang
- Yu Zhang
- Mingsheng Long
- Sebastian Ruder
- Yu Zhang
- Mingxia Liu
- Jianmin Wang
- Sinno Jialin Pan
Citations in the corpus
- A Survey on Multi-Task Learning (2017, background): “In [185], a Gaussian distribution, whose mean or covariance consists of a local compo-”