Conformalised imprecise inference for robust extrapolation under limited data

ArXi:2605.25882v1 Announce Type: new Recent advances in uncertainty quantification increasingly emphasise the distinction between aleatory and epistemic uncertainty in machine learning, motivating the need for unified frameworks. However, despite much progress in producing reliable predictions, existing methods often lack rigorous guarantees when generalising beyond the