Label-weighted conformal prediction yields finite-sample guarantees for macro-coverage and generalized macro-coverage objectives that aggregate coverage over class groupings.
arXiv preprint arXiv:2401.17452 , year=
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Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
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Conformal Prediction with Macro-Coverage Guarantees
Label-weighted conformal prediction yields finite-sample guarantees for macro-coverage and generalized macro-coverage objectives that aggregate coverage over class groupings.