{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:54Z","timestamp":1758672894672,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL), an extension of MLL to learning from label distributions, faces challenges in collecting accurate label distributions. To address the issue of biased annotations, based on the low-rank assumption, existing works recover true distributions from biased observations by exploring the label correlations. However, recent evidence shows that the label distribution tends to be full-rank, and naive apply of low-rank approximation on biased observation leads to inaccurate recovery and performance degradation. In this paper, we address the LDL with biased annotations problem from a novel perspective,  where we first degenerate the soft label distribution into a hard multi-hot label and then recover the true label information for each instance. This idea stems from an insight that assigning hard multi-hot labels is often easier than assigning a soft label distribution, and it shows stronger immunity to noise disturbances, leading to smaller label bias. Moreover, assuming that the multi-label space for predicting label distributions is low-rank offers a more reasonable approach to capturing label correlations.  Theoretical analysis and experiments confirm the effectiveness and robustness of our method on real-world datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/617","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5545-5553","source":"Crossref","is-referenced-by-count":0,"title":["Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning"],"prefix":"10.24963","author":[{"given":"Zhiqiang","family":"Kou","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"},{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingkun","family":"Xie","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuheng","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Sydney AI Centre, The University of Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan"},{"name":"Graduate School of Frontier Sciences, The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:37Z","timestamp":1758627277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/617"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/617","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}