Citations & Achievements

Citing PyOD

If you use PyOD in a scientific publication, please cite the paper that best matches your use case.

PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection (Web Conference 2025) is the preferred reference if you use LLM-based model selection, ADEngine routing, or any V3 feature:

@inproceedings{chen2025pyod,
  title={PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection},
  author={Chen, Sihan and Qian, Zhuangzhuang and Siu, Wingchun and Hu, Xingcan and Li, Jiaqi and Li, Shawn and Qin, Yuehan and Yang, Tiankai and Xiao, Zhuo and Ye, Wanghao and others},
  booktitle={Companion Proceedings of the ACM on Web Conference 2025},
  pages={2807--2810},
  year={2025}
}

The original PyOD paper (JMLR 2019, MLOSS track) remains the canonical reference for the library itself:

@article{zhao2019pyod,
  author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
  title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
  journal = {Journal of Machine Learning Research},
  year    = {2019},
  volume  = {20},
  number  = {96},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v20/19-011.html}
}

For a broader perspective on anomaly detection methodology, cite our NeurIPS benchmark papers ADBench [AHHH+22] and ADGym.

Scientific Work Using or Referencing PyOD

PyOD has been referenced and cited in hundreds of academic projects. See the Google Scholar citations for an incomplete list.

Miscellaneous