AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A family of distributional criteria inside a single honest-forest implementation of isotropic random-Fourier-feature maximum mean discrepancy, an anisotropic diagonal-bandwidth variant, an adaptive per-split frequency-selection variant, and a non-kernel sliced-Wasserstein criterion are implemented.
DiffGBM exposes the score-side recipe---residualization, EDM-style preconditioning, log-sigma time sampling, noise-level features, loss weighting, and histogram resolution---as jointly tunable axes over a shared LightGBM surface rather than one frozen bundle.
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