Stable Localized Conformal Prediction via Transduction
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. Theoretica...
| Authors | Yinjie Min, Liuhua Peng, Changliang Zou |
|---|---|
| Primary category | stat.ME |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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