AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces distributionally robust multi-objective optimization (DR-MOO), which minimizes multiple objectives under their respective worst-case distributions, and proposes Pareto-type solution concepts for DR-MOO and develops multi-gradient descent algorithms (MGDA) with provable guarantees.
This work characterize structural misspecification as a perturbed factor analysis problem, and establishes subspace identifiability under spectral separation and controlled perturbation, followed by component-wise identifiability under structural sparsity.
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