Universidad Autónoma de Madrid（马德里自治大学）；Escuela Técnica Superior de Ingeniería ICAI, Universidad Pontificia Comillas（科米亚斯主教大学ICAI高等工程技术学院）；Universidad de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；Universidad de Castilla La Mancha（卡斯蒂利亚-拉曼恰大学）
Eduardo C. Garrido-Merchán, C. Puente, A. Sobrino, J. A. Olivas
Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for representing and infering new causal information. In previous works, we have generated automatically causal graphs associated to a given concept by analyzing sets of documents and extracting and representing the found causal information in that visual way. The retrieved information shows that causality is frequently imperfect rather than exact, feature gathered by the graph. In this work we will attempt to go a step further modelling the uncertainty in the graph through probabilistic improving the management of the imprecision in the quoted graph.
SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
University of Macau（澳门大学）；Fudan University（复旦大学）；Hangzhou Dianzi University（杭州电子科技大学）；Renmin University of China（中国人民大学）；Hanyang University（翰阳大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；University of Liverpool（利物浦大学）；Southeast University（东南大学）；Central South University（中南大学）
CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China（上海智能自主系统研究院，同济大学）；Guohao School, Tongji University, Shanghai, China（国豪学院，同济大学）；School of Automotive Studies, Tongji University, Shanghai, China（汽车研究学院，同济大学）；College of Electronics and Information Engineering, Tongji University, Shanghai, China（电子与信息工程学院，同济大学）
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Oxford Robotics Institute, Dept. Engineering Science, University of Oxford, UK（牛津大学机器人研究所）；Microsoft Research, Redmond, WA, USA（微软研究院）；Bristol Robotics Laboratory, School of Engineering, University of the West of England, Bristol, UK（布里斯托尔机器人实验室）
Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure
Institute of Deep Perception Technology, JITRI（JITRI深度感知技术研究院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；Department of Electrical Engineering and Electronics, University of Liverpool（利物浦大学电气工程与电子系）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能科、智能交通科）
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels
Heidelberg University Hospital（海德堡大学医院）；Informatics for Life Institute（生命信息学研究所）；Ruprecht-Karls University Heidelberg（海德堡鲁普雷希特-卡尔斯大学）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；University of Zurich（苏黎世大学）；Mannheim University of Applied Sciences（曼海姆应用技术大学）
