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Auxiliary data enhanced conformal prediction
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AI
DOI：10.1142/S2010326326500048.png)
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Abstract
En 中文
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.
Keywords:
Conformal inference
distribution-free
finite-sample validity
uncertainty quantification
weakly(semi)-supervised learning
Journal
R
IF:
0.6
Papers:
13
Citations:
0