Shuhan Yuan

Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations

2022 · arXiv (Cornell University)

Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data. Although many approaches can achieve good performance …

Few-shot Anomaly Detection and Classification Through Reinforced Data Selection

2022 · 2022 IEEE International Conference on Data Mining (ICDM)

Due to the scarcity of anomalies, deep anomaly detection models are predominately trained in an unsupervised or semi-supervised manner depending on the availability of a small number of labeled samples. Currently, most unsupervised approaches detect …

Algorithmic Recourse in Abnormal Multivariate Time Series

2023 · arXiv (Cornell University)

Algorithmic recourse provides actionable recommendations to alter unfavorable predictions of machine learning models, enhancing transparency through counterfactual explanations. While significant progress has been made in algorithmic recourse for static data, such as tabular and image …