Confidence Set

Confidence sets are statistical constructs providing guaranteed probabilistic bounds on the true value of an unknown quantity, offering a robust approach to uncertainty quantification in machine learning predictions. Current research focuses on developing efficient and valid confidence sets for diverse applications, including image segmentation, online prediction, and sequential decision-making, often employing conformal prediction methods and Bayesian approaches to handle model uncertainty and data heterogeneity. This work is significant because reliable uncertainty quantification is crucial for building trustworthy AI systems and improving the reliability of predictions across various scientific and practical domains, such as personalized medicine and automated decision support.