Principled Weight Selection for Nonexchangeable Conformal Prediction
Determine a principled procedure for choosing the calibration weights w1,…,wn in nonexchangeable conformal prediction for nonexchangeable data, replacing heuristic choices so the method can be applied systematically in settings with covariate or distribution shift.
References
Choosing the weights is based on heuristic and remains a open question.
Finally, because we measure coverage and set size rather than downstream task harm, the safety framing concerns the statistical safety promise under shift rather than a measured task-level failure; and size-thresholded selective prediction recovers retained coverage only empirically, since the retained population is selected using the model's own output, so it is not a new finite-sample conformal guarantee. The source-invisibility conclusion is limited to the diagnostics we test; it is not an impossibility result for all nonlinear or multivariate source-derived predictors.