Achieving Counterfactual Fairness for Anomaly Detection

Author:

Han Xiao,Zhang Lu,Wu Yongkai,Yuan Shuhan

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Almanza, M., Epasto, A., Panconesi, A., Re, G.: k-clustering with fair outliers. In: WSDM. ACM (2022)

2. van Breugel, B., Kyono, T., Berrevoets, J., van der Schaar, M.: DECAF: generating fair synthetic data using causally-aware generative networks. In: NeurIPS (2021)

3. Deepak, P., Abraham, S.S.: Fair Outlier Detection. In: WISE (2020)

4. Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)

5. Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

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2. On Root Cause Localization and Anomaly Mitigation through Causal Inference;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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