The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models. Detection is generally approached by observing the drift of model performance from a global point of view. However, drifts occurring in (fine-grained) data subgroups may go unnoticed when monitoring global drift. We take a different perspective, and introduce methods for observing drift at the finer granularity of subgroups. Relevant data subgroups are identified during training and monitored efficiently throughout the model's life. Performance drifts in any subgroup are detected, quantified and characterized so as to provide an interpretable summary of the model behavior over time. Experimental results confirm that our subgroup-level drift analysis identifies drifts that do not show at the (coarser) global dataset level. The proposed approach provides a valuable tool for monitoring model performance in dynamic real-world applications, offering insights into the evolving nature of data and ultimately contributing to more robust and adaptive models.
Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；School of Mathematics and Statistics, Guangdong University of Technology（广东工业大学数学与统计学院）
Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection AI
University of Maryland（马里兰大学）；University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Advanced Computer Studies（马里兰大学高级计算机研究所）
本文评估乳腺癌检测 AI 在 EMBED 与 RSNA 2022 数据集上六类子群体的性能差异，并引入随时间漂移监测与告警机制，以发现偏见并保障跨人群有效性。
ICICLE: Interpretable Class Incremental Continual Learning
Jagiellonian University（雅盖隆大学）；Ardigen SA（阿迪根公司）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；IDEAS NCBR（IDEAS国家计算智能研究中心）
LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油与矿物大学）；IRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals（智能安全系统研究组，国王法赫德石油与矿物大学）
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis
University College London（伦敦大学学院）；Max Planck University College London Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）；Wellcome Centre for Human Neuroimaging（惠康人类神经影像中心）；UCL Queen Square Institute of Neurology（伦敦大学学院女王广场神经病学研究所）；Erasmus Medical Centre（伊拉斯姆斯医学中心）；Hospital Clinic（临床医院）
Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（信息中心部门，阜外医院，中国医学科学院和北京协和医学院）；The Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（心血管疾病临床医学研究中心，阜外医院，中国医学科学院和北京协和医学院）
