AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies the conditional empirical Rademacher complexity of deep ReLU representations followed by bounded-norm predictors in additive or L\'evy-Brownian RKHSs, termed Brownian heads, and derives an exact dual identity and sharp bounds in terms of activation mass, the average norm of the observed hidden vectors...
We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting. Building on the discrepancy principle, we propose a multi-scale stopping rule that applies to general kernel estimators and show that, unlike previous approaches, it achieves full adaptivity over...
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