{"ID":2882511,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11025","arxiv_id":"2508.11025","title":"Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks","abstract":"Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions 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.","short_abstract":"Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions with statistically valid coverage guarantees. However, current methods are often computationally expensive and data-intensive, as they require constructing an uncertainty model before calibra...","url_abs":"https://arxiv.org/abs/2508.11025","url_pdf":"https://arxiv.org/pdf/2508.11025v3","authors":"[\"Laura Lützow\",\"Michael Eichelbeck\",\"Mykel J. Kochenderfer\",\"Matthias Althoff\"]","published":"2025-08-14T19:03:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}