Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
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发表时间：
2021-11
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通讯作者：
Aryan Deshwal;J. Doppa
Aryan Deshwal;J. Doppa
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其他
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作者：
Aryan Deshwal;J. Doppa

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我们考虑优化组合空间的问题（例如，序列、树和图）使用昂贵的黑盒函数求值。例如，使用物理实验室实验优化药物设计的分子。贝叶斯优化（BO）是一种有效的框架，通过智能地选择高效用的输入指导学习代理模型来解决这些问题。组合空间的最近BO方法是通过使用深度生成模型（DGMs）学习结构的潜在表示来在连续空间上减少BO。从连续空间中选择的输入被解码成离散结构，用于执行函数求值。然而，潜在空间上的代理模型仅使用由DGM学习的信息，其可能不具有期望的归纳偏差来近似目标黑盒函数。为了克服这个缺点，本文提出了一个原则性的方法称为阶梯。其关键思想是定义一个新的结构耦合内核，明确集成的结构信息解码结构与学习的潜在空间表示更好的代理建模。我们在真实世界的基准测试中的实验表明，LADDER显着改善了潜在空间上的BO方法，并且表现得更好或类似于最先进的方法。
We consider the problem of optimizing combinatorial spaces (e.g., sequences, trees, and graphs) using expensive black-box function evaluations. For example, optimizing molecules for drug design using physical lab experiments. Bayesian optimization (BO) is an efficient framework for solving such problems by intelligently selecting the inputs with high utility guided by a learned surrogate model. A recent BO approach for combinatorial spaces is through a reduction to BO over continuous spaces by learning a latent representation of structures using deep generative models (DGMs). The selected input from the continuous space is decoded into a discrete structure for performing function evaluation. However, the surrogate model over the latent space only uses the information learned by the DGM, which may not have the desired inductive bias to approximate the target black-box function. To overcome this drawback, this paper proposes a principled approach referred as LADDER. The key idea is to define a novel structure-coupled kernel that explicitly integrates the structural information from decoded structures with the learned latent space representation for better surrogate modeling. Our experiments on real-world benchmarks show that LADDER significantly improves over the BO over latent space method, and performs better or similar to state-of-the-art methods.