Jiazhen Chen — accepted AI-conference papers

2026

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Jiazhen Chen, Xiuqin Liang, Sichao Fu, Zheng Ma, Weihua Ou

AAAI 2026technical

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years. It aims to identify anomalous data patterns using only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically assume complete node attributes and struc

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2025

Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection

Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto

ICASSP 2025accepted

Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in extracting meaningful representations from unlabeled data, its straightforward application to anomaly detection is not…

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