When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion
K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally informative under heterogeneous data. Cross Upper-Bound Validation (CUBV) replaces point-wise CV accuracy by conservative upper bounds on true risk. Here we generalise CUBV through a single exponential framework in which the moment-generating function of the generalisation gap is controlled by a cumulant envelope gamma(lambda). This yields a family of risk bounds covering Hoeffding-, Bernstein-, dependency-aware, PAC-Bayesian, and heterogeneous source-fusion settings. For K-fold CV, dependence between fold-wise gaps is modelled through a join...
| Authors | JM Gorriz |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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