Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the EM algorithm to simultaneously incorporate the Geometric Entropy Minimization principle for identifying statistical anomalies, and the Maximum Entropy Discrimination principle to incorporate utility labels, in order to detect high-utility anomalies. We apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.
A Multivariate Statistical Framework for Detection, Classification and Pre-localization of Anomalies in Water Distribution Networks
Institute of Computer Science and Information Technologies, National Technical University "Kharkiv Polytechnic Institute"（计算机科学与信息科技研究所，国家技术大学"Kharkiv 工程学院"）；Informatics Institute, University of Amsterdam（信息学院，阿姆斯特丹大学）；Bernoulli Institute, University of Groningen（伯努利研究所，格罗宁根大学）
MINES: Explainable Anomaly Detection through Web API Invariant Inference
National University of Singapore（国立新加坡大学）；Shanghai Jiao Tong University（上海交通大学）；Singapore Management University（新加坡管理学院）；GovTech Singapore（新加坡政府科技局）；Chongqing University（重庆大学）
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Objecteye Inc.（Objecteye公司）
Interpretable Maximum Margin Deep Anomaly Detection
School of Statistics and Mathematics, Yunnan University of Finance and Economics（云南财经大学统计与数学学院）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学多学科研究院）
Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（教育区块链与智能技术重点实验室，教育部，广西师范大学）
TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）
Towards Cross-domain Few-shot Graph Anomaly Detection
University of Waterloo（滑铁卢大学）；Huazhong University of Science and Technology（华中科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
