Stochastic Grouping Conformal Prediction for Effective Subgroup Reliability
Read the original on arXiv AI →Stochastic Grouping Conformal Prediction (SGCP) is a new framework that provides distribution‑free coverage guarantees while addressing coverage disparities across clinically important subgroups. SGCP learns a stochastic grouping map so that each sample can draw calibration data from others with similar calibration behavior, creating a local score law that improves reliability for all subpopulations. The authors prove that SGCP maintains the standard coverage guarantee and demonstrate through synthetic and real‑world experiments that it reduces subgroup coverage gaps while keeping prediction set sizes small or comparable to existing methods.
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