{
  "annotation_id": "954a98c8-f326-4411-9e12-804764420c0e",
  "date_created": "2026-02-17T05:53:05.109000Z",
  "date_modified": "2026-02-17T05:53:05.109000Z",
  "file_hash": "6abd60167106f84112fd5f27526646b4bf3a879462fccca35a4b8f2882fc5df1",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.06119",
    "authors": [
      "Dileepa Pitawela",
      "Gustavo Carneiro",
      "Hsiang-Ting Chen"
    ],
    "categories": [
      "cs.LG",
      "cs.AI",
      "cs.HC"
    ],
    "doi": "10.1109/ACCESS.2025.3648122",
    "journal_ref": "in IEEE Access, vol. 13, pp. 217632-217643, 2025",
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "L2CU: Learning to Complement Unseen Users",
    "url": "https://arxiv.org/abs/2601.06119",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "075d478d-5f9f-429c-9b66-2eadd4e4eb3a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}