mlr3mbo: Bayesian Optimization in R
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-hierarchica...
| Authors | Marc Becker, Lennart Schneider, Martin Binder et al. |
|---|---|
| Primary category | stat.ML |
| Published | 31 March 2026 |
| Last updated at source | 9 June 2026 |
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