Proposes SIR-based supervised dimension reduction with kernel trick for high-dimensional Bayesian optimization, claims regret bounds and empirical gains on synthetic and real tasks.
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High Dimensional Bayesian Optimization via Supervised Dimension Reduction
Proposes SIR-based supervised dimension reduction with kernel trick for high-dimensional Bayesian optimization, claims regret bounds and empirical gains on synthetic and real tasks.