{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:11:07Z","timestamp":1780765867699,"version":"3.54.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Unsupervised Domain Adaptation (UDA) aims to transfer a model from a labeled source domain to an unlabeled target domain, addressing challenges of distinct data distributions, termed domain shift. Existing UDA research primarily focuses on classification-like tasks, but neglects ranking and filtering tasks essential for applications like medical diagnosis and search engines. This paper is the first to notice and identify a new real-world transfer problem: cross-stage transfer in multi-stage cascade ranking and filtering systems, a common issue in diverse applications, including information retrieval systems, medical diagnosis, and other real-world ranking\/filtering systems. In this problem, we emphasize the crucial assumption of order-invariance and address the key issue named Cross-stage Class Concept Conflict (C4), highlighting potential inconsistencies in class concepts for the same sample at different stages. To tackle these challenges, we propose a novel method, Unsupervised Rank Adaptation (URA), comprising two key components: order-conditional distribution alignment, characterizing the order-conditional distribution intra-stage and aligning them across stages; and principal projection alignment, aligning the principal component\u2019s projection matrix with classifier parameters to ensure order-invariance without guessing pseudo-labels, mitigating the influence of C4. Experimental results show that our approach reaches state-of-the-art performance in various cross-stage transfer tasks.<\/jats:p>","DOI":"10.3233\/faia240710","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:12:32Z","timestamp":1729170752000},"source":"Crossref","is-referenced-by-count":2,"title":["Cross-Stage Transfer in Multi-Stage Cascade Ranking and Filtering Systems"],"prefix":"10.3233","author":[{"given":"Yifan","family":"Pan","sequence":"first","affiliation":[{"name":"Communication and Information Security Lab, Shenzhen Graduate School, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guibo","family":"Luo","sequence":"additional","affiliation":[{"name":"Communication and Information Security Lab, Shenzhen Graduate School, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuesheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Communication and Information Security Lab, Shenzhen Graduate School, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240710","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:12:32Z","timestamp":1729170752000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240710","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}