Uncertain Sample

Uncertain sample handling in machine learning focuses on identifying and managing data points where model confidence is low, aiming to improve reliability and safety in high-stakes applications. Current research emphasizes developing methods to quantify uncertainty, often using techniques like ensemble methods, Bayesian approaches, and uncertainty-aware mixup, and incorporating this uncertainty into model training and decision-making processes, such as through abstention or selective classification. This work is crucial for deploying machine learning models responsibly in domains like healthcare and autonomous systems, where misclassifications can have severe consequences, by improving model accuracy and robustness.