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AutoOD: Automatic Outlier Detection

Summary: AutoOD merges multiple unsupervised outlier detectors with a learned, custom outlier classifier to produce labels without ground truth. It exploits cross-detector signals to outperform the best unsupervised detector and tuning-free baselines on diverse benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6585
Venue
SIGMOD
Year
2023
Pagerank
6.9309994e-05
Overall Rank
4,062 | 72.14%
DOI
10.1145/3588700

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cao_sigmod23,
        title = {{AutoOD: Automatic Outlier Detection}},
        author = {Cao, Lei and Yan, Yizhou and Wang, Yu and Madden, Samuel and Rundensteiner, Elke A.},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588700},
        url = {https://dl.acm.org/doi/10.1145/3588700},
        year = {2023}
}

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