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

MCML Authors

Link to Profile Matthias Althoff

Matthias Althoff

Prof. Dr.

Principal Investigator

Cyber Physical Systems

Abstract

Conformal prediction is a popular uncertainty quantification method that augments a base predictor with prediction sets with statistically valid coverage guarantees. However, current methods are often computationally expensive and data-intensive, as they require constructing an uncertainty model before calibration. Moreover, existing approaches typically represent the prediction sets with intervals, which limits their ability to capture dependencies in multi-dimensional outputs. We address these limitations by introducing zono-conformal prediction, a novel approach inspired by interval predictor models and reachset-conformant identification that constructs prediction zonotopes with assured coverage. By placing zonotopic uncertainty sets directly into the model of the base predictor, zono-conformal predictors can be identified via a single, data-efficient linear program. While we can apply zono-conformal prediction to arbitrary nonlinear base predictors, we focus on feed-forward neural networks in this work. Aside from regression tasks, we also construct optimal zono-conformal predictors in classification settings where the output of an uncertain predictor is a set of possible classes. We provide probabilistic coverage guarantees and present methods for detecting outliers in the identification data. In extensive numerical experiments, we show that zono-conformal predictors are less conservative than interval predictor models and standard conformal prediction methods, while achieving a similar coverage over the test data.

misc

Preprint

Aug. 2025

Authors

L. Lützow • M. Eichelbeck • M. J. Kochenderfer • M. Althoff

Links

Research Area

 B3 | Multimodal Perception

BibTeXKey: LEK+25

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