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English(EN) Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?

新的贝叶斯优化方法平衡得分和成本

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的成本感知贝叶斯优化停止标准，称为“EI停止”。该方法旨在通过平衡最佳观测得分与每个评估步骤的成本来最大化效用。EI停止规则根据预期改进来推进候选，当预期改进不足以抵消成本时停止。虽然总体有效，但研究人员还设计了一种修正方法，以识别EI停止可能过早终止的情况，尤其是在成本较低时。 AI

影响 引入了一种更有效的超参数调整方法，可能降低AI模型开发中的计算成本。

排序理由 这是一篇详细介绍贝叶斯优化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的贝叶斯优化方法平衡得分和成本

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这是一篇详细介绍贝叶斯优化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Long Vu, Jongseong Brad Choi, Binoy Ravindran, Zhoulai Fu ·

    Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?

    arXiv:2610.42085v1 Announce Type: cross Abstract: Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test. We maximize a budget-free utility, the best observed score minus a cost per increment, with a o…

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