Cross-Calibrated Confidence Fields for Local Risk Updates

Excerpt

arXiv:2606.19147v3 Announce Type: replace-cross Abstract: 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 certifi