An Accuracy-Information Tradeoff for Loss-Difference Conditional Mutual Information
Loss-difference conditional mutual information (ld-CMI) uses the smallest of the standard observations in the supersample hierarchy of generalization bounds: it measures what a learner's loss differences reveal about which candidate of each pair it was trained on. Accuracy is known to force information into the model; data processing does not carry such lower bounds to losses. We show, by bounding three moments of the loss differences, that accuracy also forces ld-CMI. For linear predictors with a smooth convex loss of nonzero slope at zero, such as the logistic loss, plus a regularizer whose curvature and growth are both of power $r\ge2$, on product distributions over a scaled sign cube ...
| Authors | Hazar Yueksel |
|---|---|
| Primary category | cs.LG |
| Published | 6 October 2026 |
| Last updated at source | 8 October 2026 |
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