AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents provably tighter instantaneous regret bounds for GP upper confidence bound (GP-UCB) at any given iteration and proposes stopping criteria for GP-UCB based on this tighter bound that ensures an $\epsilon$-optimal solution with high probability $1-\delta$ upon termination.
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design choices consequential: the incumbent to define improvement, replication allocation, and final recommendation. Existing methods typically either...
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