Prediction Set

Prediction sets are a method in machine learning that provides a set of possible predictions instead of a single point prediction, offering a principled way to quantify uncertainty. Current research focuses on improving the efficiency and fairness of prediction sets generated by conformal prediction, a model-agnostic framework, exploring variations like decision-focused uncertainty quantification and robust methods resistant to adversarial attacks or label noise. This work is significant because it enhances the reliability and trustworthiness of machine learning models, particularly in high-stakes applications like healthcare and autonomous systems, by providing more informative and reliable uncertainty estimates.