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English(EN) CAP: Learning to Optimize from Sparse Observations of Related Tasks

新的CAP方法利用稀疏历史数据优化黑盒任务

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为CAP（Candidate Acquisition Policy）的新方法，用于在实验评估成本高昂且有限的情况下改进黑盒优化。CAP从历史数据中建模共享结构和特定任务的变体，以学习查询策略。该策略随后用于为新任务做出自适应决策，即使只有少数稀疏的历史记录。在合成任务族和工程基准（如BOPTEST和SU2）上的实验表明，在固定的评估预算下，CAP的优化性能优于其他方法。 AI

影响 该方法有望在实验评估成本高昂的领域（如工程和材料科学）实现更高效的实验设计。

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=1.0]

在 arXiv cs.LG 阅读 →

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新的CAP方法利用稀疏历史数据优化黑盒任务

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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=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lin Fu, Siwei Fu, Dong Ni ·

    CAP: Learning to Optimize from Sparse Observations of Related Tasks

    arXiv:2610.41173v1 Announce Type: new Abstract: In experiment-driven black-box optimization, each evaluation can consume materials, equipment time, and other resources, limiting the number of experiments available for a new task. Records from completed related optimization campai…

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