CLAPS: Aleatoric-Epistemic Scaling via Last-Layer Laplace for Conformal Regression

Abstract:Conformal regression provides finite-sample marginal coverage, but interval efficiency depends on how uncertainty is allocated across inputs. We propose \emph{Conformal Laplace-Aware Predictive Scaling} (CLAPS), which combines a learned heteroscedastic scale with last-layer Laplace uncertainty. The learned variance also weights the training features used to construct the posterior, yielding a noise-weighted support correction in the learned representation. The correction contracts as weighted feature information increases and becomes most influential under localized support imbalance. Experiments show near-nominal marginal coverage and modest efficiency improvements over the matched aleatoric baseline on real regression benchmarks.

Published in TMLR. OpenReview: this https URL

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2512.01384 [cs.LG]

(or arXiv:2512.01384v5 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.01384 Focus to learn more arXiv-issued DOI via DataCite

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