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cs.LG2026
TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection
Xiong Zhang, Hong Peng, Changlong Fu +3
A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of th…
cs.LG2024★ 1 cited
From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
Yuncong Yang, Xiao Han, Yidong Chai +3
Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, c…