arXiv:2509. 04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points.
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.
arXiv:2606. 31600v1 Announce Type: cross Abstract: Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees.
Conformal prediction sets, which provide finite‑sample coverage guarantees, are examined from an information‑theoretic perspective. The authors introduce a family of generalized information measures that link set size and coverage to entropy, showing that Shannon mutual information can be expressed exactly in terms of these measures. They prove that, in classification tasks, the reduction in set size due to additional information is bounded by calibration‑dependent members of this family and satisfies a data‑processing inequality, and they empirically demonstrate that set‑size reduction and mutual information can rank features differently in greedy selection.
arXiv:2601. 21455v2 Announce Type: replace-cross Abstract: Conformal prediction(CP) has become a cornerstone of distribution-free uncertainty quantification, conventionally evaluated by its coverage and interval length.
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
arXiv:2505. 19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees.
arXiv:2601. 13102v3 Announce Type: replace-cross Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estimators.