March 14, 2024, 4:41 a.m. | Yasunori Taguchi, Hiro Gangi

cs.LG updates on

arXiv:2403.08331v1 Announce Type: new
Abstract: Optimization of product and system characteristics is required in many fields, including design and control. Bayesian optimization (BO) is often used when there are high observing costs, because BO theoretically guarantees an upper bound on regret. However, computational costs increase exponentially with the number of parameters to be optimized, decreasing search efficiency. We propose a BO that limits the search region to lower dimensions and utilizes local Gaussian process regression (LGPR) to scale the BO …

abstract arxiv bayesian computational control costs cs.lg design dimensions fields however optimization product search type

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