most citedHierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

Tao Xie, Zexi Tan, Haoyi Xiao +5

Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates tran…

cs.LG20262 cited

Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers

Yiqun Zhang, Zexi Tan, Xiaopeng Luo +1

Most real-world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of outliers. In addition to sporadi…

cs.LG2025

Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering

Zexi Tan, Xiaopeng Luo, Yunlin Liu +1

Multivariate Time-Series (MTS) clustering discovers intrinsic grouping patterns of temporal data samples. Although time-series provide rich discriminative information, they also co…

cs.LG2025

DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification

Tao Xie, Zexi Tan, Haoyi Xiao +2

Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. Thi…

cs.LG2025

MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction

Zexi Tan, Tao Xie, Binbin Sun +3

Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely int…