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[2508.11025] Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
Summary
The paper introduces Zono-Conformal Prediction, a method for uncertainty quantification in regression and classification tasks that improves efficiency and captures multi-dimensional dependencies.
Why It Matters
Zono-Conformal Prediction addresses limitations of traditional conformal prediction methods, which are often computationally intensive and limited in their ability to model complex dependencies. This innovation could enhance predictive modeling in various machine learning applications, making it a significant advancement in the field.
Key Takeaways
- Zono-Conformal Prediction constructs prediction zonotopes for better uncertainty quantification.
- The method is data-efficient, requiring only a single linear program for model identification.
- It provides probabilistic coverage guarantees and is applicable to both regression and classification tasks.
- Zono-Conformal predictors are less conservative than traditional interval models while maintaining similar coverage.
- The approach can detect outliers effectively within the identification data.
Computer Science > Machine Learning arXiv:2508.11025 (cs) [Submitted on 14 Aug 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks Authors:Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer, Matthias Althoff View a PDF of the paper titled Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks, by Laura L\"utzow and Michael Eichelbeck and Mykel J. Kochenderfer and Matthias Althoff View PDF 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-co...
