贝叶斯优化
第六部分：研究前沿
EN

研究前沿

前四部分按通常的讲法介绍这些方法。本部分系统梳理截至 2026 年 9 月的文献，报告自 González 等人（2017）以来九年的研究就这些方法确立了哪些结论、哪些仍有争议、哪些尚属缺失。内容涵盖该领域的历史、人的回答背后的模型、选择查询的规则及其有记录的失效、从比较中学习的理论、方法向高维的扩展，以及影响每一项已发表结果的软件与评测实践。

将这几章合起来读，可以看出该领域如今的难点所在。十年间算法日趋成熟：查询的选择有了决策论基础，遗憾界也已追平标量反馈；而描述人如何回答的默认模型，仍停留在 2005 年的形式。瓶颈已经从算法转移到测量：一次比较究竟测量了什么，回答应当如何建模，提问本身又会对回答者产生什么影响（第 45.1 节）。

行文也随之改变。每个论断都附有证据：发表场所、样本量、得出结果的条件，以及是否经过同行评审。本书自身的推断均加以标注。每章最后将结论分为已定、有争议与缺失三类。

本部分以第四部分为前提。

本部分各章

  1. 26 偏好贝叶斯优化的十年

    从 2005 年的基线模型到 2026 年 9 月：该领域如何得名，工具与推断方法如何定型，决策论转向，以及理论迎头赶上、默认流程受到审视的那几年。交互式时间线标出每个里程碑所属的泳道与阶段。

  2. 27 观测模型、代理模型与推断

    似然对人的回答做了哪些假设，哪些代理模型取代了高斯过程及其原因，推断近似的影响有多大，以及默认实现实际做了什么。

  3. 28 采集函数、查询形式与问题扩展

    查询选择规则从启发式到决策理论的演变、多个研究组各自独立发现的失效模式、查询的各种形式与偏好贝叶斯优化的各类问题变体，以及已发表的比较为何不能简单合并：每项比较都在各自的维度与噪声水平下进行。

  4. 29 理论：从对决赌博机到核化偏好优化

    从比较中学习的已证结论：有限臂与线性对决赌博机的结果；2021 至 2026 年的核化遗憾界，及其链接函数、假设与遗憾单位；EUBO 的决策论结果；缺失的下界；观测模型的理论；可识别性；漂移、污染、反应时与停止。

  5. 30 高维问题与贝叶斯优化格局的变化

    贝叶斯优化为何曾有在 10 至 20 维以上失效之说；标量贝叶斯优化在长度尺度先验上得到了什么认识，原因何在；局部偏好方法能扩展到多高的维度，又受哪些混杂因素影响；预训练代理模型、语言模型与成本感知停止用于比较反馈时的现状。

  6. 31 软件、评测方法与研究社区

    偏好贝叶斯优化仍在维护的软件及其默认设置；研究代码为何难以重新运行；方法如何借助模拟用户评测，度量的选择为何决定胜负；换成真人回答时有何变化；以及这一领域由谁研究、分布在哪些学科、研究数量有多少。

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