In this paper, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. We discovered a condition that preserves good quality in the MSU under different combinations of these three factors, providing a new useful criterion to help drive the process of dimension reduction.
A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所——BIPS）；University of Bremen（不来梅大学）；Simula Research Laboratory（西穆拉研究实验室）；Telenor Research & Innovation（挪威电信研究与创新部）；Norwegian Computing Center（挪威计算中心）
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks
University of Pennsylvania（宾夕法尼亚大学）；University of Arizona（亚利桑那大学）；City University of Macau（澳门城市大学）；Stony Brook University（石溪大学）；Duke Kunshan University（昆山杜克大学）
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
Key Laboratory of Big Data \& Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science；Technology, Beijing Jiaotong University Beijing China；School of Computer Science；Tangshan Research Institute, Beijing Jiaotong University Tangshan China；Key Laboratory of Big Data \& Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education；Technology, Beijing Jiaotong University；Tangshan Research Institute, Beijing Jiaotong University
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Institute for Dynamical Systems and Control, ETH Zurich（动态系统与控制研究所，苏黎世联邦理工学院）；Department of Electrical Engineering, Chalmers University of Technology（电气工程系，查尔姆斯理工大学）；Department of Mechanical Engineering, Imperial College London（机械工程系，伦敦帝国理工学院）
EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting
department of Electrical Engineering and Computer Science, York University（电气工程与计算机科学系，约克大学）；School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（电子与电气工程学院，华中科技大学）；Central China Branch of State Grid Corporation of China（国家电网公司中部分部）；Department of Electronic Engineering, University of Rome Tor Vergata（罗马大学Tor Vergata电子工程系）；Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)（国家大学间电信研究会（CNIT））
