SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
arXiv preprint arXiv:2502.17264 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Label-weighted conformal prediction yields finite-sample guarantees for macro-coverage and generalized macro-coverage objectives that aggregate coverage over class groupings.
citing papers explorer
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Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
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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.