No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels
Finite-Library Input-Warped Bayesian Optimization (FLIWBO), which selects warps from a finite library of smooth input maps by any history-dependent rule, and adapts the input geometry to accelerate learning while retaining high-probability convergence guarantees under mild hypotheses, with an explicit library-size cost...
Edvin Ketabati Augustinsson, Robert A. Bridges
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