Better Uncertainty Estimation

Better uncertainty estimation in machine learning aims to improve the reliability and trustworthiness of model predictions by quantifying their uncertainty. Current research focuses on developing model-agnostic techniques, employing ensembles of models (like U-Nets and ViTs), and leveraging techniques such as Bayesian methods and statistical depth measures to achieve more accurate uncertainty quantification, particularly in challenging scenarios like limited data, out-of-distribution samples, and noisy labels. This improved understanding of model uncertainty is crucial for deploying machine learning models in high-stakes applications across diverse fields, from medical imaging and soil science to natural language processing and computer vision, enhancing safety and decision-making.