Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks

Researchers introduce zono-conformal prediction, a new uncertainty quantification method for machine learning that uses zonotope-based prediction sets instead of traditional intervals. The approach is more computationally efficient and less conservative than existing conformal prediction methods while maintaining statistical coverage guarantees for both regression and classification tasks.