Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However, in practice this technique is used limitedly since such grouping information is usually hidden. In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward this, we formulate a joint optimization problem in the task parameter and the group membership, by constructing an appropriate regularizer to encourage sparse learning as well as correct recovery of task groups. We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.
Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity
School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；School of Statistics and Data Science, Southeast University（东南大学统计与数据科学学院）；Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences（中国科学院大数据挖掘与知识管理重点实验室）；Department of Biostatistics, Yale School of Public Health（耶鲁大学公共卫生学院生物统计学系）
Sparsely Grouped Input Variables for Neural Networks
Yale Child Study Center, School of Medicine, Yale University（耶鲁大学医学院耶鲁儿童研究中心）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Seattle Children’s Research Institute（西雅图儿童研究所）；School of Medicine, University of Washington（华盛顿大学医学院）
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
The Alan Turing Institute（艾伦·图灵研究所）；University of Cambridge（剑桥大学）；Scania CV（斯堪尼亚商用车公司）；Scania AB (publ)（斯堪尼亚公司（上市））；Stockholm University（斯德哥尔摩大学）；Imperial College London（伦敦帝国理工学院）
