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 <https://arxiv.org/abs/2412.12154>`_ (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 <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ (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 <https://arxiv.org/abs/2206.09426>`_ :cite:`a-han2022adbench` and `ADGym <https://arxiv.org/abs/2309.15376>`_.


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Scientific Work Using or Referencing PyOD
-----------------------------------------

PyOD has been referenced and cited in hundreds of academic projects. See the `Google Scholar citations <https://scholar.google.com/scholar?oi=bibs&hl=en&cites=3726241381117726876>`_ for an incomplete list.


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Featured Posts & Tutorials
--------------------------

Articles, tutorials, and workshops that feature PyOD:

* **Analytics Vidhya**: `An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_
* **KDnuggets**: `Intuitive Visualization of Outlier Detection Methods <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_
* **KDnuggets**: `An Overview of Outlier Detection Methods from PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>`_
* **Towards Data Science**: `Anomaly Detection for Dummies <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_
* **Computer Vision News**: `Python Open Source Toolbox for Outlier Detection <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_
* **awesome-machine-learning**: `General-Purpose Machine Learning <https://github.com/josephmisiti/awesome-machine-learning#python-general-purpose>`_
* **Dr. Hadi Fanaee (lecture)**: `Anomaly Detection Lecture <https://www.youtube.com/watch?v=sF2DeSPrGfc>`_
* **Dr. Kiri Wagstaff (NASA/JPL KISS workshop)**: `Detecting the Unexpected: An Introduction to Anomaly Detection Methods <http://www.kiss.caltech.edu/workshops/technosignatures/presentations/Wagstaff.pdf>`_  (`video <https://www.youtube.com/watch?v=brWqY4Wads4>`_)


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Miscellaneous
-------------

* `PapersWithCode: Anomaly Detection <https://paperswithcode.com/task/anomaly-detection>`_
* `awesome-python <https://github.com/uhub/awesome-python>`_