Conformalised imprecise inference for robust extrapolation under limited data

2026-05-25

AI Summary

This work addresses the challenge that existing machine learning methods struggle to distinguish between epistemic and aleatoric uncertainty under extrapolation scenarios and often lack rigorous coverage guarantees. The authors propose a model-agnostic conformal fuzzy inference framework that, for the first time, integrates conformal prediction with fuzzy reasoning. By incorporating a distance-aware mechanism, the method generates fuzzy predictions—formally represented as probability boxes—with theoretical validity guarantees. This approach enables adaptive uncertainty quantification under distributional shift and demonstrates superior robustness and reliable coverage compared to conventional probabilistic methods on both synthetic and benchmark datasets, with particularly pronounced advantages in data-scarce regimes.

Abstract

Recent advances in uncertainty quantification increasingly emphasise the distinction between aleatory and epistemic uncertainty in machine learning, motivating the need for more unified frameworks. However, despite much progress in producing reliable predictions, existing methods often lack rigorous guarantees when generalising beyond the training domain. We propose a conformalised imprecise inference framework for robust extrapolation, which is model-agnostic and augments predictive models with imprecision and distance awareness. The proposed approach yields imprecise predictions (probability boxes) that remain valid under distributional shift, maintaining coverage while adaptively expanding uncertainty in extrapolation regimes. Experiments on synthetic and benchmark datasets demonstrate improved robustness and reliable coverage compared to standard probabilistic approaches, particularly under limited data.

Problem

• extrapolation
• distributional shift
• uncertainty quantification
• limited data
• coverage

Innovation

• conformal prediction
• imprecise probability
• distributional shift
• epistemic uncertainty
• robust extrapolation