AUC-Oriented Domain Adaptation: From Theory to Algorithm

doi:10.1109/TPAMI.2023.3303943

	AUC-Oriented Domain Adaptation: From Theory to Algorithm
	Yang, Zhiyong 1; Xu, Qianqian 2; Bao, Shilong 3,4; Wen, Peisong 1,2; He, Yuan 5; Cao, Xiaochun 6; Huang, Qingming 7,8,9
	2023-12-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	45 期号:12 页码:14161-14174
摘要	The Area Under the ROC curve (AUC) is a crucial metric for machine learning, which is often a reasonable choice for applications like disease prediction and fraud detection where the datasets often exhibit a long-tail nature. However, most of the existing AUC-oriented learning methods assume that the training data and test data are drawn from the same distribution. How to deal with domain shift remains widely open. This paper presents an early trial to attack AUC-oriented Unsupervised Domain Adaptation (UDA) (denoted as AUCUDA hence after). Specifically, we first construct a generalization bound that exploits a new distributional discrepancy for AUC. The critical challenge is that the AUC risk could not be expressed as a sum of independent loss terms, making the standard theoretical technique unavailable. We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function. Turning theory into practice, the original discrepancy requires complete annotations on the target domain, which is incompatible with UDA. To fix this issue, we propose a pseudo-labeling strategy and present an end-to-end training framework. Finally, empirical studies over five real-world datasets speak to the efficacy of our framework.
关键词	AUC-oriented Learning domain adaptation machine learning
DOI	10.1109/TPAMI.2023.3303943
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[62025604] ; National Natural Science Foundation of China[6212200758] ; National Natural Science Foundation of China[61976202] ; National Natural Science Foundation of China[62206264] ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001130146400008
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38360
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China 4.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China 5.Alibaba Grp, Secur Dept, Hangzhou 311121, Peoples R China 6.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China 7.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 8.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 9.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Zhiyong,Xu, Qianqian,Bao, Shilong,et al. AUC-Oriented Domain Adaptation: From Theory to Algorithm[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):14161-14174.
APA	Yang, Zhiyong.,Xu, Qianqian.,Bao, Shilong.,Wen, Peisong.,He, Yuan.,...&Huang, Qingming.(2023).AUC-Oriented Domain Adaptation: From Theory to Algorithm.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),14161-14174.
MLA	Yang, Zhiyong,et al."AUC-Oriented Domain Adaptation: From Theory to Algorithm".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):14161-14174.