Structured Sparse Multi-Task Learning with Generalized Group Lasso

doi:10.3233/FAIA230333

	Structured Sparse Multi-Task Learning with Generalized Group Lasso
	Fei, Luhuan1 ; Sun, Lu1 ; Kudo, Mineichi 2; Kimura, Keigo 2
	2023-09-28
会议录名称	FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
ISSN	0922-6389
卷号	372
页码	692-699
DOI	10.3233/FAIA230333
摘要	Multi-task learning (MTL) improves generalization by sharing information among related tasks. Structured sparsity-inducing regularization has been widely used in MTL to learn interpretable and compact models, especially in high-dimensional settings. These methods have achieved much success in practice, however, there are still some key limitations, such as limited generalization ability due to specific sparse constraints on parameters, usually restricted in matrix form that ignores high-order feature interactions among tasks, and formulated in various forms with different optimization algorithms. Inspired by Generalized Lasso, we propose the Generalized Group Lasso (GenGL) to overcome these limitations. In GenGL, a linear operator is introduced to make it adaptable to diverse sparsity settings, and helps it to handle hierarchical sparsity and multi-component decomposition in general tensor form, leading to enhanced flexibility and expressivity. Based on GenGL, we propose a novel framework for Structured Sparse MTL (SSMTL), that unifies a number of existing MTL methods, and implement its two new variants in shallow and deep architectures, respectively. An efficient optimization algorithm is developed to solve the unified problem, and its effectiveness is validated by synthetic and real-world experiments. © 2023 The Authors.
会议录编者/会议主办者	Amazon Alexa ; APTIV ; et al. ; Hewlett Packard ; IDEAS ; Software Force
关键词	Learning systems Machine learning Mathematical operators Optimization Compact model Generalisation Group lassos High-dimensional Learning to learn Multitask learning Optimization algorithms Regularisation Sharing information Structured sparsities
会议名称	26th European Conference on Artificial Intelligence, ECAI 2023
会议地点	Krakow, Poland
会议日期	September 30, 2023 - October 4, 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	IOS Press BV
EI入藏号	20234515035141
EI主题词	Linearization
EISSN	1879-8314
EI分类号	723.4 Artificial Intelligence ; 921.5 Optimization Techniques
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/346431
专题	信息科学与技术学院_PI研究组_孙露组信息科学与技术学院_硕士生
通讯作者	Fei, Luhuan
作者单位	1.ShanghaiTech University, China; 2.Hokkaido University, Japan
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Fei, Luhuan,Sun, Lu,Kudo, Mineichi,et al. Structured Sparse Multi-Task Learning with Generalized Group Lasso[C]//Amazon Alexa, APTIV, et al., Hewlett Packard, IDEAS, Software Force:IOS Press BV,2023:692-699.