研究人员开发了改进贝叶斯优化（一种用于优化复杂函数的技术）的新方法。一种方法，动态共享嵌入贝叶斯优化（DSEBO），可自动调整搜索空间的维度，以更有效地处理高维问题。另一种方法，核发现，利用 LLM 自动生成和选择这些优化任务的最佳核函数，性能优于现有基线。第三个框架 BOOST，可自动联合选择核函数和采集函数，在各种优化场景中表现出鲁棒性。 AI
影响 贝叶斯优化的这些进展可能导致在各种 AI 应用中更高效、更有效地调整复杂模型和系统。
排序理由 多篇研究论文提出贝叶斯优化新方法。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
arXiv:2605.23473v1 Announce Type: cross Abstract: Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective d…
Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional su…
arXiv:2605.20249v1 Announce Type: cross Abstract: Gaussian Process (GP) kernels are central to Bayesian optimization (BO), yet designing effective kernels for high-dimensional problems still relies on extensive manual engineering. Existing automated approaches struggle in high di…
arXiv:2508.02332v4 Announce Type: replace-cross Abstract: The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functio…