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PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery

Summary: PACE learns task decomposition for healthcare using a reject option routing easy tasks to model and hard tasks to clinicians. Two-level design: Self-Paced Learning selects easy tasks; micro revision tunes weights for better easy-task accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6234
Venue
SIGMOD
Year
2021
Pagerank
5.4453394e-05
Overall Rank
8,352 | 42.70%
DOI
10.1145/3448016.3457281

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zheng_sigmod21,
        title = {{PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery}},
        author = {Zheng, Kaiping and Chen, Gang and Herschel, Melanie and Ngiam, Kee Yuan and Ooi, Beng Chin and Gao, Jinyang},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457281},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457281},
        year = {2021}
}

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