Human-in-the-loop Outlier Detection
Summary: HOD combines machine outliers with human validation to reveal true outliers beyond statistical rarity. Context-inlier clustering aids interpretation; a bipartite-graph selector minimizes queries while covering all candidates, with empirical gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chengliang Chai (Tsinghua University)
- 2. Lei Cao (Massachusetts Institute of Technology)
- 3. Guoliang Li (Tsinghua University)
- 4. Jian Li (Tsinghua University)
- 5. Yuyu Luo (Tsinghua University)
- 6. Samuel Madden (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{chai_sigmod20,
title = {{Human-in-the-loop Outlier Detection}},
author = {Chai, Chengliang and Cao, Lei and Li, Guoliang and Li, Jian and Luo, Yuyu and Madden, Samuel},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389772},
url = {https://dl.acm.org/doi/10.1145/3318464.3389772},
year = {2020}
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