Generalization Bounds via Vector-Valued $L_p$ Correlation $V_{\alpha }$

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 $V_{\alpha }$, and derive generalization bounds whose dependence terms interpolate between total variation (TV) at $\alpha =1$ and variance and ${\chi }^2$ related behavior at $\alpha =2$. For $\alpha >2$, 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 $\alpha =2$, where the bound can be expressed in terms of the variability of the algorithm’s output density across datasets, $\int \sqrt{{\mathrm{V}\mathrm{a}\mathrm{r}}_S[p(w\mid S)]}dw$. We show that the entire $V_{\alpha }$ 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 (yielding rates that depend on the inverse temperature and sample size). To address scaling, we extend the framework to a conditional formulation using supersamples, analogous in spirit to conditional mutual information (CMI), and discuss evaluations in the output space as a practical surrogate for estimating dependence. Simulations on Bayesian linear regression illustrate how tuning $\alpha$ can change the tightness of the resulting bounds across regimes. Additional supersample experiments on noisy MNIST and on a pretrained ResNet-50 for CIFAR-10 show similar behavior of the projected loss-space bounds beyond the original clean-MNIST setting.