Conformal Prediction

Epistemic Uncertainty in Conformal Scores: A Unified Approach
WE introduce EPICSCORE, a model-agnostic method that enhances conformal prediction by integrating epistemic uncertainty. Compatible with any Bayesian model and maintaining distribution-free guarantees, EPICSCORE adapts prediction intervals based on data availability, achieving both finite-sample marginal and asymptotic conditional coverage.
L. M. C. Cabezas, V. S. Santos, T. R. Ramos, Rafael Izbicki
May, 2025 Proceedings of Machine Learning Research (UAI Track; Oral Presentation)
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Epistemic Uncertainty in Conformal Scores: A Unified Approach
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
A. Fröhlich, T. Ramos, G. Cabello, I. Buzatto, Rafael Izbicki, D. Tiezzi
February, 2025 In AAAI
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CD-split and HPD-split: Efficient conformal regions in high dimensions
Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. We introduce CD-split and HPD-split, which yield general prediction regions and converge to the optimal highest predictive density set.
Rafael Izbicki, Gilson Shimizu, Rafael B. Stern
May, 2022 Journal of Machine Learning Research
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CD-split and HPD-split: Efficient conformal regions in high dimensions
Flexible distribution-free conditional predictive bands using density estimators
Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. Besides average coverage, one …
Gilson Shimizu, Rafael Izbicki, Rafael B. Stern
April, 2020 In PMLR
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