AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work establishes the first curvature-independent regret guarantees for decentralized online optimization on Hadamard manifolds, and studies Distributed Riemannian Online Gradient Descent, which combines local Riemannian h-subgradient updates with an implicit Fr\'echet-mean consensus.
This paper offers theoretical insights into when MCR yields a more favorable finite-sample estimation-error upper bound, viewed through the lens of neural network distance, and proposes a novel practical diagnostic that leverages the duality gap and a calibrated drift estimator to infer the potential benefit of MCR tra...
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