Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models

תקציר מקורי באנגליתarXiv:2607.21636v2 Announce Type: replace-cross Abstract: Synthetic tabular data is prized for preserving not just each column's marginal distribution but the dependencies between columns - structure that carries much of the discriminative signal for minority classes in imbalanced domains such as fraud detection and clinical risk. Yet the metrics most commonly used to certify such data are largely blind to this structure: a fully-factorized baseline that destroys all inter-column dependency is judged nearly indistinguishable from real data by the logistic-regression C2ST, while the pairwise Trend score is only partially sensitive. We introduce a dependency-aware fidelity diagnostic that decomposes a strong gradient-boosted classifier two-sample test (XGB-C2ST) into marginal, dependency, an