Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences

ArXi:2603.20025v1 Announce Type: cross We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(f,\Gamma)$-divergences, we prove a new infimal subadditivity principle showing that, under suitable conditions, a global variational discrepancy is controlled by an average of family-level discrepancies aligned with the graph. In an additive regime, this surrogate is exact.