Uncertainty Modeling

Uncertainty modeling in machine learning aims to quantify and represent the inherent uncertainty in predictions, improving model reliability and interpretability. Current research focuses on developing methods to decompose uncertainty into aleatoric (data-driven) and epistemic (model-driven) components, often employing techniques like Bayesian neural networks, stochastic differential equations, and information-theoretic approaches within various architectures including graph neural networks and Koopman operators. This work is crucial for enhancing the trustworthiness of AI systems across diverse applications, from autonomous driving and robotics to medical image analysis and scientific modeling, where understanding and managing uncertainty is paramount for safe and reliable decision-making.