BOKE uses kernel regression and density-based exploration in a confidence-bound acquisition function to achieve quadratic-complexity Bayesian optimization with a claimed global convergence guarantee under noise.
Ada-BKB: scalable Gaussian process op- timization on continuous domains by adaptive discretization
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Bayesian Optimization by Kernel Regression and Density-based Exploration
BOKE uses kernel regression and density-based exploration in a confidence-bound acquisition function to achieve quadratic-complexity Bayesian optimization with a claimed global convergence guarantee under noise.