Localized conformal prediction can now be used with formal guarantees that it provides accurate uncertainty estimates for individual test points, not just on average, with clear bounds showing how localization bandwidth and calibration size affect performance.
This paper improves conformal prediction—a method that adds uncertainty bands to any AI model—by making those bands more accurate for specific data points rather than just on average.