{"type":"ScholarlyArticle","title":"One-step Label Shift Adaptation via Robust Weight Estimation","authors":[{"name":"Ruidong Fan","slug":"ruidong-fan","given":"Ruidong","family":"Fan","affiliation":""},{"name":"Xiao Ouyang","slug":"xiao-ouyang","given":"Xiao","family":"Ouyang","affiliation":""},{"name":"Tingjin Luo","slug":"tingjin-luo","given":"Tingjin","family":"Luo","affiliation":""},{"name":"Lijun Zhang","slug":"lijun-zhang","given":"Lijun","family":"Zhang","affiliation":""},{"name":"Chenping Hou","slug":"chenping-hou","given":"Chenping","family":"Hou","affiliation":""}],"abstract":"Label shift is a prevalent phenomenon encountered in open environments, characterized by a notable discrepancy in the label distributions between the source (training) and target (test) domains, whereas the conditional distributions given the labels remain invariant. Existing label shift methods adopt a two-step strategy: initially computing the importance weight and subsequently utilizing it to calibrate the target outputs. However, this conventional strategy overlooks the intricate interplay between output adjustment and weight estimation. In this paper, we introduce a novel approach termed as One-step Label Shift Adaptation (OLSA). Our methodology jointly learns the predictive model and the corresponding weights through a bi-level optimization framework, with the objective of minimizing an upper bound on the target risk. To enhance the robustness of our proposed model, we incorporate a debiasing term into the upper-level classifier training and devise a regularization term for the lower-level weight estimation. Furthermore, we present theoretical analyses about the generalization bounds, offering guarantees for the model's performance. Extensive experimental results substantiate the efficacy of our proposal.","doi":"10.24963/ijcai.2025/565","url":"https://ijcai.org/proceedings/2025/565","pdf":"https://ijcai.org/proceedings/2025/0565.pdf","markdown":"https://ijcai.org/proceedings/2025/565.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/565","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":565,"pages":{"first":5074,"last":5082},"keywords":["Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Learnware/model reuse/transfer learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}