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Yi-Meng Lu

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Book Open access Aug 2026

SCALE: Style-Causal Disentanglement with Adaptive Lifelong Expert for Online Latent-domain Anomaly Detection

Time-series anomaly detection in real-world streams is often challenged by evolving operating conditions, where distribution shifts can be easily mistaken for anomalies. Due to this, we study a new problem, online latent-domain anomaly detection, where domain labels and shift times are unobserved, the number of domains...

Yi-Meng Lu, Yi-Fei Gao, Tian Lan et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection

Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different detection criteria. While time-series foundation models provide transferable representations, coupling them with a fixed anomaly-scoring mechan...

Tian Lan, Yi-Fei Gao, Yi-Meng Lu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

Experiments show that CAPS achieves the strongest aggregate performance across all four evaluation metrics among the compared methods, while complementary ablations and transfer analyses support the roles of context anchoring, semantic disentanglement, and conditional realization.

Yi-Fei Gao, Tian Lan, Yi-Meng Lu et al. · 0 citations

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