Abstract:Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibration data. In practice, when only one calibration set of limited size is available, prediction sets often exhibit high variability in size, especially for methods with localization. We formalize this concern as set stability, defined as the variance of the conditional expectation of the set size given the calibration data. To improve stability without requiring additional target-task labels, we propose Stable Conformal Prediction (StCP), a transfer learning approach that utilizes labeled source-task data and unlabeled target data. Theoretically, we characterize the marginal coverage and stability of StCP; empirically, it delivers more stable prediction sets than standard conformal prediction methods, especially for those with localization, when calibration data are limited.
| Subjects: | Methodology (stat.ME); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.01452 [stat.ME] |
| (or arXiv:2605.01452v1 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2605.01452 arXiv-issued DOI via DataCite (pending registration) |
From: Yinjie Min [view email]
[v1]
Sat, 2 May 2026 14:02:16 UTC (176 KB)
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