@inproceedings{Hennhofer2024,
	title        = {{ Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors }},
	author       = {Hennhofer, Oliver and Preisach, Christine},
	year         = 2024,
	month        = {Dec},
	booktitle    = {2024 IEEE International Conference on Knowledge Graph (ICKG)},
	publisher    = {IEEE Computer Society},
	address      = {Los Alamitos, CA, USA},
	pages        = {110--119},
	doi          = {10.1109/ICKG63256.2024.00022},
	url          = {https://doi.ieeecomputersociety.org/10.1109/ICKG63256.2024.00022}
}

@article{Bates2023,
	title        = {Testing for outliers with conformal p-values},
	author       = {Bates,  Stephen and Candès,  Emmanuel and Lei,  Lihua and Romano,  Yaniv and Sesia,  Matteo},
	year         = 2023,
	month        = feb,
	journal      = {The Annals of Statistics},
	publisher    = {Institute of Mathematical Statistics},
	volume       = 51,
	number       = 1,
	doi          = {10.1214/22-aos2244},
	issn         = {0090-5364},
	url          = {http://dx.doi.org/10.1214/22-AOS2244}
}

@article{Jin2023,
    author = {Jin, Ying and Candès, Emmanuel J},
    title = {Model-free selective inference under covariate shift via weighted conformal p-values},
    journal = {Biometrika},
    pages = {asaf066},
    year = {2025},
    month = {09},
    abstract = {This paper introduces novel weighted conformal p-values and methods for model-free selective inference. The problem is as follows: given test units with covariates X and missing responses Y, how do we select units for which the responses Y are larger than user-specified values while controlling the proportion of false positives? Can we achieve this without modelling assumptions on the data and without restriction on the model for predicting the responses? Last, methods should be applicable when there is a covariate shift between training and test data, which commonly occurs in practice. We answer these questions by first leveraging any prediction model to produce a class of well-calibrated weighted conformal p-values, which control the type-I error in detecting a large response. These  p-values cannot be passed onto classical multiple testing procedures since they may not obey a well-known positive dependence property. Hence, we introduce weighted conformal selection, a new procedure that controls the false discovery rate in finite samples. Besides prediction-assisted candidate selection, weighted conformal selection (i) allows us to infer multiple individual treatment effects, and (ii) extends to outlier detection with inlier distribution shifts. We demonstrate performance via simulations and applications to causal inference, drug discovery and outlier detection datasets.},
    issn = {1464-3510},
    doi = {10.1093/biomet/asaf066},
    url = {https://doi.org/10.1093/biomet/asaf066},
    eprint = {https://academic.oup.com/biomet/advance-article-pdf/doi/10.1093/biomet/asaf066/64237131/asaf066.pdf},
}


@inproceedings{Laxhammar2010,
author = {Laxhammar, Rikard and Falkman, G\"{o}ran},
title = {Conformal prediction for distribution-independent anomaly detection in streaming vessel data},
year = {2010},
isbn = {9781450302265},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1833280.1833287},
doi = {10.1145/1833280.1833287},
booktitle = {Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques},
pages = {47–55},
numpages = {9},
keywords = {anomaly detection, conformal prediction, sea surveillance},
location = {Washington, D.C.},
series = {StreamKDD '10}
}

@phdthesis{Tax2001,
title = "One-class classification; concept-learning in the absence of counter-examples",
author = "DMJ Tax",
note = "ASCI Dissertation Series 65",
year = "2001",
isbn = "90-75691-05-X",
type = "Dissertation (TU Delft)",
school = "Delft University of Technology",
}

@inbook{Papadopoulos2002,
  title = {Inductive Confidence Machines for Regression},
  ISBN = {9783540367550},
  ISSN = {0302-9743},
  url = {http://dx.doi.org/10.1007/3-540-36755-1_29},
  DOI = {10.1007/3-540-36755-1_29},
  booktitle = {Machine Learning: ECML 2002},
  publisher = {Springer Berlin Heidelberg},
  author = {Papadopoulos,  Harris and Proedrou,  Kostas and Vovk,  Volodya and Gammerman,  Alex},
  year = {2002},
  pages = {345–356}
}

@book{Vovk2005,
title = "Algorithmic Learning in a Random World",
author = "Vladimir Vovk and Alex Gammerman and Glenn Shafer",
note = "Springer, New York",
year = "2005",
language = "English",
publisher = "Springer",
isbn = {0387001522},
url = {http://dx.doi.org/10.1007/b106715},
DOI = {10.1007/b106715},
}

@article{Lei2012,
    author = {Lei, Jing and Wasserman, Larry},
    title = {Distribution-free prediction bands for non-parametric regression},
    journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
    volume = {76},
    number = {1},
    pages = {71-96},
    year = {2013},
    month = {07},
    issn = {1369-7412},
    doi = {10.1111/rssb.12021},
    url = {https://doi.org/10.1111/rssb.12021},
    eprint = {https://academic.oup.com/jrsssb/article-pdf/76/1/71/49514328/jrsssb\_76\_1\_71.pdf},
}

@article{Zhao2019,
  doi = {10.48550/ARXIV.1901.01588},
  url = {https://arxiv.org/abs/1901.01588},
  author = {Zhao,  Yue and Nasrullah,  Zain and Li,  Zheng},
  keywords = {Machine Learning (cs.LG),  Information Retrieval (cs.IR),  Machine Learning (stat.ML),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {PyOD: A Python Toolbox for Scalable Outlier Detection},
  publisher = {arXiv},
  year = {2019},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

@inproceedings{Zhao2024,
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 Zhang, Yichi and Dong, Yushun and Zhao, Yue},
title = {PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection},
year = {2025},
isbn = {9798400713316},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3701716.3715196},
doi = {10.1145/3701716.3715196},
booktitle = {Companion Proceedings of the ACM on Web Conference 2025},
pages = {2807–2810},
numpages = {4},
location = {Sydney NSW, Australia},
series = {WWW '25}
}

@article{Benjamini1995,
author = {Benjamini, Yoav and Hochberg, Yosef},
title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
volume = {57},
number = {1},
pages = {289-300},
doi = {10.1111/j.2517-6161.1995.tb02031.x},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1995.tb02031.x},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1995.tb02031.x},
abstract = {SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.},
year = {1995}
}

@article{Benjamini2009,
  title = {Selective inference in complex research},
  volume = {367},
  ISSN = {1471-2962},
  url = {http://dx.doi.org/10.1098/rsta.2009.0127},
  DOI = {10.1098/rsta.2009.0127},
  number = {1906},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical,  Physical and Engineering Sciences},
  publisher = {The Royal Society},
  author = {Benjamini,  Yoav and Heller,  Ruth and Yekutieli,  Daniel},
  year = {2009},
  month = nov,
  pages = {4255–4271}
}

@misc{Tibshirani2019,
  doi = {10.48550/ARXIV.1904.06019},
  url = {https://arxiv.org/abs/1904.06019},
  author = {Tibshirani,  Ryan J. and Barber,  Rina Foygel and Candes,  Emmanuel J. and Ramdas,  Aaditya},
  keywords = {Methodology (stat.ME),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Conformal Prediction Under Covariate Shift},
  publisher = {arXiv},
  year = {2019},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

@ARTICLE{Jon2023,
  title     = "Model-free selective inference under covariate shift via
               weighted conformal p-values",
  author    = "Jin, Ying and Cand{\`e}s, Emmanuel J",
  journal   = "Biometrika",
  publisher = "Oxford University Press (OUP)",
  doi = {10.1093/biomet/asaf066},
  number    = "asaf066",
  month     =  sep,
  year      =  2025,
  language  = "en"
}


@article{Liang2024,
    author = {Liang, Ziyi and Sesia, Matteo and Sun, Wenguang},
    title = {Integrative conformal p-values for out-of-distribution testing with labelled outliers},
    journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
    volume = {86},
    number = {3},
    pages = {671-693},
    year = {2024},
    month = {01},
    issn = {1369-7412},
    doi = {10.1093/jrsssb/qkad138},
    url = {https://doi.org/10.1093/jrsssb/qkad138},
    eprint = {https://academic.oup.com/jrsssb/article-pdf/86/3/671/58504432/qkad138.pdf},
}
