AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Natural gradient descent (NGD) underlies common methods in ML. For dually flat families, idealized NGD on the forward Kullback--Leibler objective follows the mixture geodesic which is often longer than the shortest Fisher--Rao path. We quantify this overhead by the inefficiency ratio \(R \ge 1\), the Fisher length of t...
A path-level regret bound for PathUCB that decomposes regret over suboptimal paths via a per-path complexity C(pi) combining each edge's prefix and suffix reliability, complementary to the edge-level CCB bound and more informative on structured graphs with polynomially many source-to-goal paths.
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