Sketched Calibration for Conformal Prediction under Covariate Shift
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
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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.