Publications (4)
A Graph-based Framework for Online Time Series Anomaly Detection Using Model Ensemble
Zewei Yu, Jianqiu Xu, Caimin Li
With the increasing volume of streaming data in industrial systems, online anomaly detection has become a critical task. The diverse and rapidly evolving data patterns pose signifi…
Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling
Wei Hu, Zewei Yu, Jianqiu Xu
Multivariate time-series (MTS) anomaly detection is critical in domains such as service monitor, IoT, and network security. While multi-model methods based on selection or ensembli…
Stop Unnecessary Reflection: Training LRMs for Efficient Reasoning with Adaptive Reflection and Length Coordinated Penalty
Zewei Yu, Lirong Gao, Yuke Zhu +4
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks by employing test-time scaling. However, they often generate over-long chains-of-t…
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
Mingxuan Xia, Haobo Wang, Yixuan Li +4
Recently, Large Language Models (LLMs) have demonstrated significant potential for data annotation, markedly reducing the labor costs associated with downstream applications. Howev…