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Creators/Authors contains: "Wu, Yongkai"

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  1. ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
  2. ; ; ; ( , Proceedings of the 32nd ACM International Conference on Information and Knowledge Management)
    Free, publicly-accessible full text available October 21, 2024
  3. ; ; ( , IEEE)
    Free, publicly-accessible full text available June 18, 2024
  4. ; ; ; ( , Pacific-Asia Conference on Knowledge Discovery and Data Mining)
    Free, publicly-accessible full text available May 27, 2024
  5. ; ; ; ( , Advances in Knowledge Discovery and Data Mining, PAKDD 2023)
    Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detection approaches mainly focus on association-based fairness notions. In this work, we target counterfactual fairness, which is a prevalent causation-based fairness notion. The goal of counterfactually fair anomaly detection is to ensure that the detection outcome of an individual in the factual world is the same as that in the counterfactual world where the individual had belonged to a different group. To this end, we propose a counterfactually fair anomaly detection (CFAD) framework which consists of two phases, counterfactual data generation and fair anomaly detection. Experimental results on a synthetic dataset and two real datasets show that CFAD can effectively detect anomalies as well as ensure counterfactual fairness. 
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    Free, publicly-accessible full text available May 27, 2024
  6. ; ; ( , 2022 IEEE International Conference on Big Data (Big Data))
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  7. ; ; ; ; ( , 2022 IEEE International Conference on Big Data (Big Data))
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  8. ; ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  9. ; ; ; ( , Companion of The 2020 Web Conference)
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  10. ; ; ; ( , Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020)
    null (Ed.)
    Accepted Manuscript Full Text Available