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AUC-Oriented Domain Adaptation: From Theory to Algorithm

文献类型：期刊论文

作者Yang, Zhiyong9; Xu, Qianqian8; Bao, Shilong6,7; Wen, Peisong8,9; He, Yuan5; Cao, Xiaochun4; Huang, Qingming1,2,3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-12-01
卷号45期号:12页码:14161-14174
关键词AUC-oriented Learning domain adaptation machine learning
ISSN号0162-8828
DOI10.1109/TPAMI.2023.3303943
英文摘要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.
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
语种英语
WOS记录号WOS:001130146400008
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38359]  
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.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
4.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China
5.Alibaba Grp, Secur Dept, Hangzhou 311121, Peoples R China
6.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China
7.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China
8.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
9.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, 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.

入库方式： OAI收割

来源：计算技术研究所

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