Cross-Calibrated Confidence Fields for Local Risk Updates
How can training data be used to compare local updates to the current model, choose an update, and retain valid bounds for the selected update's population-risk change? We construct lower and upper confidence fields that jointly cover the population-risk change of every update in a possibly continuous local update space. The fields can therefore be used both to compare the updates and to select an update; a negative upper endpoint certifies improvement over the reference model. For linear risk changes in possibly infinite-dimensional feature spaces, cross-calibration uses the discrepancy between two balanced folds to calibrate the full-sample estimation error. Under covariance-aligned sub...
| Authors | Mingzhi Song |
|---|---|
| Primary category | stat.ML |
| Published | 17 June 2026 |
| Last updated at source | 13 August 2026 |
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