English(EN) Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions

新的OGPIT方法通过自适应复制改进随机函数优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一种名为OGPIT（Optimization by Gaussian Processes In Trust regions）的新方法，用于优化随机函数，特别是高方差的函数。该方法结合了局部建模和自适应复制，允许系统将重复评估策略性地分配到最有益的区域。数值实验表明，与现有方法相比，OGPIT可以显著提高计算效率并保持解决方案的准确性，尤其是在考虑评估成本时。 AI

影响这项研究可能导致更高效的AI模型训练和优化，尤其适用于复杂或有噪声的目标函数。

排序理由该集群包含一篇详细介绍新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Mickael Binois (ACUMES), Jeffrey Larson (ANL) · 2026-09-03 04:00

随机函数基于信任域的贝叶斯优化中的自适应复制策略

arXiv:2504.20527v3 Announce Type: replace-cross Abstract: We develop and analyze a method for stochastic simulation optimization based on Gaussian process models within a trust-region framework. We focus on settings where the variance of the objective function is large, making ac…

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随机函数基于信任域的贝叶斯优化中的自适应复制策略

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