Learning Feature Selection Dependencies in Multi-task Learning

By Daniel Hernández-Lobato, José Miguel Hernández-Lobato. 2013

41 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Qiang Yang
2. Yu Zhang
3. Mingsheng Long
4. Jianmin Wang
5. Dit-Yan Yeung
6. Yu Zhang
7. Dongrui Wu
8. Sinno Jialin Pan

Citations in the corpus

• An Overview of Multi-task Learning (2018, methodology): “The difference between [16] and [17] is that in [16], the horseshoeprior is generalized to learn feature covariance, while in [17], the horseshoe prior is used as a basic prior and the whole model is to identify outlier tasks in a way different from [15].”
• A Survey on Multi-Task Learning (2017, background): “In [35], a generalized horseshoe prior is proposed to do feature selection for MTL as:”