Generalization Bounds via Vector-Valued $L_p$ Correlation $V_α$

Mohammad Hossein Momeni Hamaneh, Mohammad Mahdi Mojahedian, Mohammad Hossein Yassaee

Understanding generalization of randomized learning algorithms remains a central challenge. Mutual-information and total-variation bounds provide complementary ways to quantify data--hypothesis dependence, but they can behave quite differently in high-dimensional regimes. We study a one-parameter family of dependence measures, the vector-valued $L_p$-norm correlation $V_α$, and derive generalization bounds whose dependence terms interpolate between total variation (TV) at $α=1$ and variance and $χ^2$ related behavior at $α=2$. For $α>2$, the Hölder-conjugate loss moment is below two, yielding heavy-tailed specializations that can remain finite even when the loss variance is infinite. A particularly interpretable specialization occurs at $α=2$, where the bound can be expressed in terms of the variability of the algorithm's output density across datasets, $\int \sqrt{\mathrm{Var}_S[p(w\mid S)]}~ dw$. We show that the entire $V_α$ family is invariant under smooth bijective reparameterizations of the hypothesis space. We also introduce an adaptive density stability (ADS) condition that implies explicit control of $V_2$, and we verify it for Gibbs-type posteriors under bounded losses…