AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is proved that any batch-level sample-agnostic linear estimator with a shared modulation parameter incurs an irreducible bias with respect to the clean-data gradient, and that sample-level all-or-nothing gating is the unique unbiased strategy within a natural distribution-free estimator class.
AdaOGD-PFS is proposed, an adaptive-step-size method that achieves O(sqrt(G_T) regret with per-round feasibility while preserving per-round feasibility and identifies a nonnegative Polyak correction P_T that enters the regret bound with a negative sign.
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