用生成模型直接采样优化候选解，实现大规模高维黑箱优化。
Generative Bayesian Optimization: Generative Models as Acquisition Functions
- 用观测值直接训练生成模型，使其采样分布正比于贝叶斯优化的采集函数。
- 支持大规模批量、高维及组合优化，无需构建代理回归模型。
- 适用于各类奖励信号，适合高维复杂场景的自动化设计优化。
我们提出一种通用策略，将生成模型转化为批量贝叶斯优化中的候选解采样器。利用生成模型进行优化可实现大规模批量处理、非连续设计空间以及高维和组合设计问题的求解。受直接偏好优化（DPO）启发，我们证明可通过从观测中直接计算的噪声简单效用值来训练生成模型，从而形成其采样密度与期望效用（即贝叶斯优化采集函数值）成比例的提议分布。该方法还可推广至基于偏好反馈以外的一般奖励信号与损失函数。这一视角避免了传统方法中需构建代理模型（回归或分类）的步骤。理论上，我们证明在特定条件下，贝叶斯优化过程中的生成模型序列渐近逼近最优目标分布。我们在涉及高维大批次的挑战性优化问题上进行了实验验证。
原文摘要 · Abstract (English)
We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large batch scaling as generative sampling, optimization of non-continuous design spaces, and high-dimensional and combinatorial design. Inspired by the success of direct preference optimization (DPO), we show that one can train a generative model with noisy, simple utility values directly computed from observations to then form proposal distributions whose densities are proportional to the expected utility, i.e., BO's acquisition function values. Furthermore, this approach is generalizable beyond preference-based feedback to general types of reward signals and loss functions. This perspective avoids the construction of surrogate (regression or classification) models, common in previous methods that have used generative models for black-box optimization. Theoretically, we show that the generative models within the BO process follow a sequence of distributions which asymptotically approximate an optimal target under certain conditions. We also evaluate the performance through experiments on challenging optimization problems involving large batches in high dimensions.
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