Uncertainty Quantification in KANs: A Conformalized Approach for Robust Prediction

On April 21, 2025, researchers introduced Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning, detailing a novel approach to enhance the reliability of machine learning models through advanced uncertainty quantification techniques.

The paper introduces uncertainty quantification methods for Kolmogorov-Arnold Networks (KANs) using ensemble approaches and conformal prediction. Conformalized KANS combine KAN ensembles with distribution-free conformal prediction to generate calibrated prediction intervals with guaranteed coverage. Numerical experiments demonstrate the robustness and accuracy of these methods under various hyperparameters, showing their applicability to recent KAN extensions like Finite Basis KANS (FBKANS) and multi-fidelity KANS (MFKANS). The results highlight potential improvements in the reliability and practicality of KANs for scientific applications.

The article discusses Bayesian Kolmogorov-Arnold-Gabor Networks (BKANs), a novel machine learning model that integrates Bayesian principles with KANs to address limitations in traditional deterministic models.

This structured approach highlights the potential of BKANs while identifying areas for deeper exploration to fully understand their capabilities and applications.