💡 Anomaly Detection

[NeurIPS 2025 Spotlight] Self-Perturbed Anomaly-Aware Graph Dynamics for Multivariate Time-Series Anomaly Detection,
Jinyu Cai, Yuan Xie, Glynnis Lim, Yifang Yin, Roger Zimmermann, See-Kiong Ng
Advances in Neural Information Processing Systems, 2025. (CCF A)

[ICML 2025 Spotlight] Leveraging Diffusion Model as Pseudo-Anomalous Graph Generator for Graph-Level Anomaly Detection,
Jinyu Cai, Yunhe Zhang, Fusheng Liu, See-Kiong Ng
Proceedings of the International Conference on Machine Learning, 2025. (CCF A)

[ICML 2025] Self-Discriminative Modeling for Anomalous Graph Detection,
Jinyu Cai, Yunhe Zhang, Jicong Fan
Proceedings of the International Conference on Machine Learning, 2025. (CCF A)

[ACM MM 2024] Towards Effective Federated Graph Anomaly Detection via Self-Boosted Knowledge Distillation,
Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-Kiong Ng
Proceedings of the 32nd ACM International Conference on Multimedia, Pages 5537-5546, 2024. (CCF A) | [code]

[IJCAI 2024] LG-FGAD: An Effective Federated Graph Anomaly Detection Framework,
Jinyu Cai, Yunhe Zhang, Jicong Fan, See-Kiong Ng
Proceedings of the 33rd International Joint Conference on Artificial Intelligence, Pages 3760-3769, 2024. (CCF A) | [code]

[ICLR 2024] Deep Orthogonal Hypersphere Compression for Anomaly Detection,
Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan
Proceedings of the 12th International Conference on Learning Representations, 2024. | [code]

[NeurIPS 2022] Perturbation Learning based Anomaly Detection,
Jinyu Cai, Jicong Fan
Advances in Neural Information Processing Systems, 2022. (CCF A) | [code]