5 papers · 1 filter
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
Yinsong Chen, Samson S. Yu, Kashem M. Muttaqi
Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conflates epistemic and aleatori…
A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification
Yinsong Chen, Samson S. Yu, Zhong Li +1
Post-hoc explainable AI (XAI) methods usually return one attribution map, even when the model represents uncertainty in its parameters. We define the \emph{explanation distribution…
Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation
Yinsong Chen, Samson S. Yu, Kashem M. Muttaqi
Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and p…
Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario
Yinsong Chen, Kaifeng Wang, Xiaoqiang Meng +3
Current research on decision-making in safety-critical scenarios often relies on inefficient data-driven scenario generation or specific modeling approaches, which fail to capture…
Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximation
Yinsong Chen, Samson S. Yu, Zhong Li +1
In recent years, inconsistency in Bayesian deep learning has attracted significant attention. Tempered or generalized posterior distributions are frequently employed as direct and…