Auxiliary Data Enhanced Conformal Prediction

Conformal prediction (CP) offers a principled framework for quantifying predictive uncertainty with finite-sample coverage guarantees. However, when the calibration data are limited, the coverage of CP sets can deviate substantially from the nominal target. This paper introduces Enhanced Conformal Prediction (ECP), a new framework that incorporates abundant but potentially corrupted auxiliary data to recalibrate prediction sets by left-shifting the score thresholds derived from the clean calibration set, provably reducing set size while preserving finite-sample coverage guarantees. Further theoretical analysis demonstrates that ECP achieves a higher breakdown point than existing methods. Extensive experiments confirm the robustness and efficiency of ECP across a variety of settings.