潜在贝叶斯优化：基于自回归归一化流的方法
Latent Bayesian Optimization via Autoregressive Normalizing Flows
AI总结：
针对潜在贝叶斯优化中的价值差异问题，提出基于归一化流的NF-BO方法，通过SeqFlow自回归流消除重构差距，并采用动态探索概率采样策略，在分子生成任务上显著优于现有方法。
摘要：
贝叶斯优化（BO）已被公认在优化昂贵且复杂的目标函数方面具有有效性。近期潜在贝叶斯优化（LBO）的进展通过整合生成模型（如变分自编码器（VAEs））来管理高维和结构化数据空间的复杂性，显示出潜力。然而，现有的LBO方法常常面临价值差异问题，该问题源于输入空间与潜在空间之间的重构差距。这种价值差异问题会在整个优化过程中传播错误，导致次优结果。为解决此问题，我们提出了一种基于归一化流的贝叶斯优化（NF-BO），该方法利用归一化流作为生成模型，建立从输入空间到潜在空间的一对一编码函数，以及其左逆解码函数，从而消除重构差距。具体而言，我们引入了SeqFlow，一种用于序列数据的自回归归一化流。此外，我们开发了一种新的候选采样策略，该策略根据每个令牌的重要性动态调整其探索概率。通过大量实验，我们的NF-BO方法在分子生成任务中表现出优越性能，显著优于传统和近期LBO方法。
英文摘要：
Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (LBO) have shown promise by integrating generative models such as variational autoencoders (VAEs) to manage the complexity of high-dimensional and structured data spaces. However, existing LBO approaches often suffer from the value discrepancy problem, which arises from the reconstruction gap between input and latent spaces. This value discrepancy problem propagates errors throughout the optimization process, leading to suboptimal outcomes. To address this issue, we propose a Normalizing Flow-based Bayesian Optimization (NF-BO), which utilizes normalizing flow as a generative model to establish one-to-one encoding function from the input space to the latent space, along with its left-inverse decoding function, eliminating the reconstruction gap. Specifically, we introduce SeqFlow, an autoregressive normalizing flow for sequence data. In addition, we develop a new candidate sampling strategy that dynamically adjusts the exploration probability for each token based on its importance. Through extensive experiments, our NF-BO method demonstrates superior performance in molecule generation tasks, significantly outperforming both traditional and recent LBO approaches.
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