Isotonic Conformal Prediction
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. We consider two objectives for reliable uncertainty quantification: self-calibration, requiring a point prediction to be unbiased conditional on its own value, and prediction-conditional validity, requiring a prediction interval to attain nominal coverage conditional on the prediction. Self-Calibrating Conformal Prediction (SC-CP) attains both objectives exactly in finite samples, but requires refitting its calibrator for every candidate outcome, which is computationally prohibitive for continuous outcomes. We propose Iso...
| Authors | Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk et al. |
|---|---|
| Primary category | stat.ML |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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