2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision
Chen Liu, Shibo He, Haoyu Liu +1
Time series anomaly detection (TSAD) plays a vital role in various domains such as healthcare, networks, and industry. Considering labels are crucial for detection but difficult to…
Label-Free Multivariate Time Series Anomaly Detection
Qihang Zhou, Shibo He, Haoyu Liu +2
Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which i…
Detecting Multivariate Time Series Anomalies with Zero Known Label
Qihang Zhou, Jiming Chen, Haoyu Liu +2
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However,…
Fairness-aware Outlier Ensemble
Haoyu Liu, Fenglong Ma, Shibo He +2
Outlier ensemble methods have shown outstanding performance on the discovery of instances that are significantly different from the majority of the data. However, without the aware…