AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Conformal-DRO is proposed, which uses nested conformal regions to construct an ambiguity set for the future latent law, which covers this law with probability at least $1-\alpha$ in finite samples, without estimating underlying latent laws or their mixing mechanism.
A decision-aware conformal framework that learns uncertainty sets tailored to robust optimization objectives by directly minimizing the induced robust loss, while preserving statistical validity through conformal calibration is proposed.
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