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 α, and derive generalization bounds whose dependence terms interpolate between total variation (TV) at α=1 and variance and χ squared 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, integral sqrt Var S[p(w 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 (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 α 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.