R语言的贝叶斯优化工具箱，支持多目标与并行计算。
mlr3mbo: Bayesian Optimization in R
- 模块化设计，可灵活构建自定义优化算法。
- 在YAHPO Gym上表现媲美前沿优化器如Optuna、SMAC3。
- 适合需要高效调参的机器学习研究者使用。
我们介绍mlr3mbo，一个用于R语言的贝叶斯优化模块化工具箱。该工具支持单目标与多目标优化、多点提案、批量及异步并行化，具备稳健的错误处理能力。尽管适用于多种标准贝叶斯优化场景，研究人员也可利用其灵活组件构建定制算法。本文除介绍软件及其设计原则与组件外，还在基于代理模型的基准套件YAHPO Gym上进行了两项深入的实证评估：通过坐标下降搜索mlr3mbo配置空间，识别出适用于数值型与混合层次型优化的稳健默认配置，并分析各设置的影响；此外，还将mlr3mbo与HEBO、SMAC3、Ax和Optuna等主流优化器进行对比，结果表明其性能达到当前领先水平。
原文摘要 · Abstract (English)
We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous parallelization, and robust error handling. While it can be used for many standard Bayesian optimization variants in applied settings, researchers can also construct custom Bayesian optimization algorithms from its flexible building blocks. In addition to an introduction to the software, its design principles, and its building blocks, the paper presents two extensive empirical evaluations on the surrogate-based benchmark suite YAHPO Gym. To identify robust default configurations for both numeric and mixed-hierarchical optimization regimes, and to gain further insights into the respective impacts of individual settings, we run a coordinate descent search over the mlr3mbo configuration space and analyze its results. Furthermore, we benchmark mlr3mbo against a wide range of established optimizers, including HEBO, SMAC3, Ax, and Optuna, and find that it performs on par with state-of-the-art.
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