arXiv:2509. 04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points.
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arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
The paper extends conformal prediction by introducing a geometric view of uncertainty through credal sets, which are families of probability distributions compatible with CP regions. It provides analytical characterisations and efficient algorithms for common credal uncertainty measures, reducing complexity from exponential to linear after sorting p-values. The authors also unify these measures with classical conformal summaries using Tsallis entropy penalties, prove an impossibility theorem for split-conformal regression, and demonstrate through experiments that the new summaries recover ranking information lost by prediction-set size, enabling finer-grained uncertainty assessment.
Conformal prediction is a popular tool for uncertainty quantification that outputs prediction sets with finite-sample coverage guarantees. While prediction set size is commonly used as a heuristic mea...
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
arXiv:2603.23923v2 Announce Type: replace-cross Abstract: Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution an...
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.