No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyperparameter optimization (HPO) and multi-agent system (MAS) design. Convergence-rate guarantees exist for select methods, notably GP upper confidence bound (GP-UCB), but require a fixed kernel. Critically, the...
Edvin Ketabati Augustinsson, Robert A. Bridges
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