English(EN) Taking the GP Out of the Loop

新的认知最近邻方法加速了贝叶斯优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-06 04:00

研究人员开发了认知最近邻（ENN）方法，这是一种旨在提高贝叶斯优化（BO）在具有大量观测值的问题上的可扩展性的新方法。与面临数据量立方级扩展问题的传统高斯过程（GPs）不同，ENN为拟合和获取提供了线性扩展方法。这种新方法集成到TuRBO-ENN框架中，显著减少了提议时间，在处理多达50,000个观测值时，比现有方法提高了1到2个数量级。 AI

影响这项研究可以为具有大型数据集的复杂机器学习模型实现更高效的超参数调整和优化。

排序理由该集群包含一篇关于贝叶斯优化新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇关于贝叶斯优化新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mehul Bafna, Siddhant anand Jadhav, David Sweet · 2026-05-06 04:00

让GP脱离循环

arXiv:2506.12818v3 Announce Type: replace Abstract: Bayesian optimization (BO) has traditionally solved black-box problems where function evaluation is expensive and, therefore, observations are few. Recently, however, there has been growing interest in applying BO to problems wh…

本文如何被排名

报道来源 [1]

让GP脱离循环

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