Isotonic Conformal Prediction: Efficient Uncertainty Quantification with Self-Calibration
A new framework called Isotonic Conformal Prediction (ICP) is introduced for uncertainty quantification, achieving both self-calibration and prediction-conditional validity. The framework includes two procedures: Split ICP (SICP), which provides asymptotic self-calibration at low computational cost, and Transductive ICP (TICP), which achieves exact finite-sample guarantees without repeated refitting. Experiments on synthetic and real-world datasets show that ICP matches the coverage of previous methods while significantly reducing computational overhead.
Why it matters: ICP enables practical and reliable uncertainty quantification for continuous outcomes by reducing computational demands while maintaining strong calibration guarantees.
Full story at: arXiv Statistical ML ↗