LOCUS: A Distribution-Free Loss-Quantile Score for Risk-Aware Predictions
Modern machine learning models can be accurate on average yet still make mistakes that dominate deployment cost. We introduce Locus, a distribution-free wrapper that produces a per-input loss-scale reliability score for a fixed prediction function. Rather than quantifying uncertainty about the label, Locus models the realized loss of the prediction function using any engine that outputs a predictive distribution for the loss given an input. A simple split-calibration step turns this function into a distribution-free interpretable score that is comparable across inputs and can be read as an upper loss level. The score is useful on its own for ranking, and it can optionally be thresholded t...
| Authors | Matheus Barreto, Mário de Castro, Thiago R. Ramos et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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