Introduction to Deep Evidential Regression for Uncertainty Quantification

Machine learning models can be dangerously overconfident, as they often lack a reliable way to express uncertainty when encountering unfamiliar data. Deep Evidential Regression (DER) is a framework that allows a neural network to rapidly quantify its own uncertainty in a single forward pass. Instead of a simple output, the model learns to predict the parameters of a higher-order distribution, which models the evidence supporting a prediction. This technique efficiently separates uncertainty into its epistemic (model-based) and aleatoric (data-based) components, a task that is computationally expensive for other methods.