arXiv:2608.23638v1 Announce Type: cross Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration...
arXiv:2511.15146v2 Announce Type: replace Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straightforwa...
arXiv:2608.21262v1 Announce Type: cross Abstract: Many machine-learning systems set a threshold at a quantile of a calibration set: conformal predictors that promise 90% coverage by drawing their cut...
arXiv:2609.30886v1 Announce Type: new Abstract: Conformal prediction can lose coverage when the data distribution changes after deployment. We study adaptation using labeled source data and unlabeled...
The paper studies how coarsening a calibrated probability vector—by converting it to a hard label such as an argmax or a confidence threshold—affects the estimation of a treatment effect vector τ in a partially linear regression setting. It shows that the plug‑in estimator converges to a distorted version Δτ, where the distortion operator ΔΔ depends on the regression of the discarded part of the score on the retained part. The authors derive how this distortion drives coverage loss of Wald confidence intervals, provide estimable formulas for the bias and coverage, and demonstrate severe loss in simulations and real‑data audits.
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.
arXiv:2608. 01460v1 Announce Type: new Abstract: Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs), providing prediction sets with finite-sample coverage guarantees under exchangeability.
Threshold-Aware Conformal Routing (TACR) is a method that decides whether to use a fast surrogate model or a full simulator based on whether the surrogate’s conformal interval lies entirely on one side of a fixed decision threshold. TACR learns input‑dependent interval widths that are tighter near the threshold, preserving distribution‑free coverage while reducing the number of simulator calls. Experiments on scientific and engineering datasets show TACR cuts simulator deferrals by 14‑75% compared to standard conformal prediction, and by an additional 10‑24% over a variant without threshold‑aware weighting.
The paper introduces a selection‑safe conformal early‑exit method that calibrates a single split‑conformal quantile using a terminal‑path score derived from prefix maxima. This approach guarantees coverage for any data‑dependent stopping policy that preserves terminal‑set containment, without requiring an α/L correction. Empirical results on eight encoder backbones and CIFAR‑100 show that pathwise selection achieves higher macro coverage (0.901 vs. 0.814) with smaller set sizes and lower latency fractions compared to naive layer‑wise selection, and remains robust to deployment‑time budget changes.
CS-WCP introduces confidence‑set weighted conformal prediction to provide robust prediction sets for large‑language‑model judges when deployment traffic shifts the prevalence of task or policy groups. By constructing simultaneous exact intervals for source and target group masses and taking the union over all compatible ratio vectors, CS‑WCP achieves high coverage (mean 0.973) with few failures across 336 constructed traffic shifts, outperforming standard source conformal prediction. The method offers an auditable coverage safeguard under uncertain mixture weights, focusing on conservative tail protection rather than tighter set sizes.
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.