论文标题
贝叶斯优化中有条件参数空间的添加树结构协方差函数
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
论文作者
论文摘要
贝叶斯优化（BO）是用于评估昂贵的黑框函数的样品有效的全局优化算法。关于条件参数空间中基于模型的优化的现有文献通常建立在树上。在这项工作中，我们将添加剂假设推广到树结构函数，并提出一个加性树结构的协方差函数，显示出提高的样品效率，更广泛的适用性和更大的灵活性。此外，通过将参数空间的结构信息和BO循环中的加性假设结合在一起，我们开发了一种并行算法来优化采集函数，并且可以在低维空间中执行此优化。我们在优化基准函数以及神经网络模型压缩问题上演示了我们的方法，实验结果表明，我们的方法明显胜过当前的有条件参数优化的最新技术状态，包括SMAC，TPE和Jenatton等人。 （2017）。
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, as well as on a neural network model compression problem, and experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).
