论文标题
老板：贝叶斯对弦空间的优化
BOSS: Bayesian Optimization over String Spaces
论文作者
论文摘要
本文开发了一种贝叶斯优化方法（BO）方法，该方法直接在原始字符串上起作用，提出了BO循环中弦核和遗传算法的首次使用。 BO上串线的最新应用已被映射到平滑而无约束的潜在空间的必要性受到阻碍。学习此预测是计算和数据密集型的。相反，我们的方法基于字符串内核构建了强大的高斯流程替代模型，自然支持可变长度输入，并对具有句法约束的空间进行有效的采集功能。实验表明，对广泛约束的现有方法的优化大大提高，包括语法受到无上下文语法控制的流行环境。
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. Recent applications of BO over strings have been hindered by the need to map inputs into a smooth and unconstrained latent space. Learning this projection is computationally and data-intensive. Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactical constraints. Experiments demonstrate considerably improved optimization over existing approaches across a broad range of constraints, including the popular setting where syntax is governed by a context-free grammar.
