Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences
We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(f,Γ)$-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, the surrogate is exact. This closes a theoretical gap in the literature; existing subadditivity results justify graph-informed adversarial learning for classical discrepancies, but not for interpolative divergences, where the usual factorization argument breaks down. In turn, we provide a justificat...
| Authors | Panagiota Birmpa, Eric Joseph Hall |
|---|---|
| Primary category | stat.ML |
| Published | 20 March 2026 |
| Last updated at source | 2 April 2026 |
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