Conformal Prediction Set

Conformal prediction sets offer a distribution-free method for generating prediction intervals or sets that contain the true value with a user-specified probability, providing a valuable measure of uncertainty for machine learning models. Current research focuses on improving the efficiency and robustness of these sets, addressing issues like fairness, adapting to distribution shifts, and handling high-dimensional data or specific model architectures such as graph neural networks and deep learning models. This work is significant because it enhances the trustworthiness and reliability of machine learning predictions, particularly in high-stakes applications where understanding uncertainty is crucial for informed decision-making, including human-in-the-loop systems and medical diagnosis.