From the 1 of 13 linked papers with an AI index.
13 papers
CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection
Yunfeng Zhao, Qingfeng Chen, Yue Tan +4
The paper introduces CORE, a unified approach for detecting anomalies in tabular data that aligns heterogeneous features into a common space and uses in-context reconstruction of n…
Towards Anomaly Detection on Relational Data
Shiyuan Li, Yunfeng Zhao, Yue Tan +3
Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and…
Towards One-for-All Anomaly Detection for Tabular Data
Shiyuan Li, Yixin Liu, Yu Zheng +3
Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods f…
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3
Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering…
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
Hongjiang Chen, Xin Zheng, Yixin Liu +7
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the under…
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
Yixin Liu, Shiyuan Li, Yu Zheng +4
Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…