Predicting silent failures of learned inference under restricted access: what measurement physics and prior information explain
Learned priors are used to reconstruct regions that measurements barely reach, and to report uncertainty there. They can fail silently: the reported interval excludes the truth while every ground-truth-free diagnostic stays within its noise. We call such errors the blind set and ask whether they can be predicted before the target data are seen. For a correctly specified linear-Gaussian model averaged over the prior, the risk of a silent failure factorises exactly into non-detection and non-coverage, which does not hold for learned systems in general; we build a score that approximates this reference by linearising a VAE prior. In a prospective study on real X-ray CT objects (2DeteCT) unde...
| Authors | Mario Gianni |
|---|---|
| Primary category | stat.ML |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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