RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
Deep isolation forest for anomaly detection.IEEE Transactions on Knowledge and Data Engineering, 35(12):12591–12604, 2023a
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Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.
citing papers explorer
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RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
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Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
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TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.