Conformal Predictor

Conformal prediction is a model-agnostic framework for generating prediction sets, rather than single predictions, that come with guaranteed coverage probabilities at a user-defined significance level. Current research focuses on improving the efficiency and applicability of conformal predictors, including developing weighted aggregation methods for improved accuracy and exploring adaptive techniques to handle non-exchangeable data and heteroscedastic noise, often employing algorithms like Mondrian conformal prediction or leveraging ensemble methods. This robust uncertainty quantification approach is increasingly valuable across diverse fields, from machine translation and earth observation to improving the efficiency of existing machine learning models and enhancing human-in-the-loop decision-making systems.