The performance of machine learning models can be impacted by changes in data over time. A promising approach to address this challenge is invariant learning, with a particular focus on a method known as invariant risk minimization (IRM). This technique aims to identify a stable data representation that remains effective with out-of-distribution (OOD) data. While numerous studies have developed IRM-based methods adaptive to data augmentation scenarios, there has been limited attention on directly assessing how well these representations preserve their invariant performance under varying conditions. In our paper, we propose a novel method to evaluate invariant performance, specifically tailored for IRM-based methods. We establish a bridge between the conditional expectation of an invariant predictor across different environments through the likelihood ratio. Our proposed criterion offers a robust basis for evaluating invariant performance. We validate our approach with theoretical support and demonstrate its effectiveness through extensive numerical studies.These experiments illustrate how our method can assess the invariant performance of various representation techniques.
Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与形成中心）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid Shandong Electric Power Company Jiaozhou Power Supply Company（山东电网济南供电分公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）；State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University（可再生能源电力系统国家重点实验室，华北电力大学电气与电子工程学院）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电气工程系）
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）；National University of Singapore（新加坡国立大学）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Molecular and Clinical Ophthalmology, Basel（巴塞尔分子与临床眼科研究所）；Abyss Processing Pte Ltd（深渊处理私人有限公司）
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology（工业和信息化部智能防灾减灾重点实验室）；School of Civil Engineering, Harbin Institute of Technology（哈尔滨工业大学土木工程学院）；Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education（教育部结构动力行为与控制重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors
University of Southern California（南加州大学）；Texas A&M University（得克萨斯农工大学）；Virginia Tech（弗吉尼亚理工大学）
Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology（国家交通大学科学与信息工程系）；Research Center for Information Technology Innovation, Academia Sinica（学术院资讯科技创新研究中心）；University of Palermo（巴勒莫大学）；Institute of Information Science, Academia Sinica（学术院资讯科学研究所）；Department of Electrical Engineering, Chung Yuan Christian University（Chung Yuan Christian University电子工程系）
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；MS Center Amsterdam（阿姆斯特丹多发性硬化中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Leiden University（莱顿大学）；Delft University of Technology（代尔夫特理工大学）
