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Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions
High-dimensional Bayesian optimization (BO) often fits Gaussian process (GP) surrogates from far fewer observations than input dimensions. Modern Vanilla BO can perform well in this regime with dimension-aware priors, initialization, and acquisition optimization, but it typically retains automatic r
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