Evidential Regression

Evidential regression is a machine learning approach focusing on simultaneously predicting a target variable and quantifying its associated uncertainty, addressing limitations of traditional methods that often provide only point estimates. Current research emphasizes Deep Evidential Regression (DER) networks, often incorporating modifications like novel regularization techniques to improve uncertainty estimation and prediction accuracy. This framework finds applications across diverse fields, including video analysis, molecular modeling, and emotion recognition, offering improved robustness and reliability in high-stakes applications where understanding uncertainty is crucial.