High Epistemic Uncertainty

High epistemic uncertainty, representing uncertainty reducible with more data or improved models, is a critical focus in machine learning, particularly for high-stakes applications like medical imaging and weather forecasting. Current research emphasizes developing methods to quantify and disentangle epistemic uncertainty from aleatoric uncertainty (inherent randomness), often employing Bayesian neural networks, ensembles of models (including diffusion models), and novel risk-sensitive reinforcement learning algorithms. This work aims to improve model reliability and robustness, leading to more trustworthy predictions and decision-making across diverse fields, including bias mitigation in computer vision and natural language processing.