GRAPE: Gradient refinement and progress-aware exploitation for query-efficient high-dimensional Bayesian optimization
Read the original at arxiv.org→arXiv:2608.25116v1 Announce Type: new Abstract: Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learning and scientific discovery. While local Bayesian...
Original headline: "GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization"
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- Aug 27, 04:00 UTC arXiv cs.LG lead source GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization