{"ID":2823143,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.06119","arxiv_id":"2601.06119","title":"L2CU: Learning to Complement Unseen Users","abstract":"Recent research highlights the potential of machine learning models to learn to complement (L2C) human strengths; however, generalizing this capability to unseen users remains a significant challenge. Existing L2C methods oversimplify interaction between human and AI by relying on a single, global user model that neglects individual user variability, leading to suboptimal cooperative performance. Addressing this, we introduce L2CU, a novel L2C framework for human-AI cooperative classification with unseen users. Given sparse and noisy user annotations, L2CU identifies representative annotator profiles capturing distinct labeling patterns. By matching unseen users to these profiles, L2CU leverages profile-specific models to complement the user and achieve superior joint accuracy. We evaluate L2CU on datasets (CIFAR-10N, CIFAR-10H, Fashion-MNIST-H, Chaoyang and AgNews), demonstrating its effectiveness as a model-agnostic solution for improving human-AI cooperative classification.","short_abstract":"Recent research highlights the potential of machine learning models to learn to complement (L2C) human strengths; however, generalizing this capability to unseen users remains a significant challenge. Existing L2C methods oversimplify interaction between human and AI by relying on a single, global user model that negle...","url_abs":"https://arxiv.org/abs/2601.06119","url_pdf":"https://arxiv.org/pdf/2601.06119v1","authors":"[\"Dileepa Pitawela\",\"Gustavo Carneiro\",\"Hsiang-Ting Chen\"]","published":"2026-01-03T20:30:48Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.HC\"]","methods":"[]","has_code":false}