Distribution-informed Online Conformal Prediction

Conformal prediction is a framework for quantifying uncertainty in machine learning predictions, crucial for reliable real-world applications. This paper introduces an online conformal prediction method that incorporates distribution information, aiming to improve the efficiency and applicability of uncertainty quantification in dynamic data streams.