CLAPS: Posterior-Aware Conformal Intervals via Last-Layer Laplace
This provides a practical upgrade for uncertainty quantification in regression, particularly beneficial for applications with limited data, though it is incremental over existing conformal baselines.
The paper tackles the problem of generating accurate prediction intervals in regression by introducing CLAPS, a method that combines Last-Layer Laplace Approximation with conformal calibration to align intervals with predictive uncertainty. The result is narrower intervals at target coverage, especially on small to medium tabular datasets, with consistent nominal coverage and improved efficiency across benchmarks.
We present CLAPS, a posterior-aware conformal regression method that pairs a Last-Layer Laplace Approximation with split-conformal calibration. From the resulting Gaussian posterior, CLAPS defines a simple two-sided posterior CDF score that aligns the conformity metric with the full predictive shape, not just a point estimate. This alignment yields narrower prediction intervals at the same target coverage, especially on small to medium tabular datasets where data are scarce and uncertainty modeling matters. We also provide a lightweight diagnostic suite that separates aleatoric and epistemic components and visualizes posterior behavior, helping practitioners understand why intervals shrink when they do. Across multiple benchmarks using the same MLP backbone, CLAPS consistently attains nominal coverage with improved efficiency and minimal overhead, offering a clear, practical upgrade to residual-based conformal baselines.