mlr3mbo: New Bayesian Optimization Toolbox Released for R
Researchers have released mlr3mbo, a modular toolbox for Bayesian optimization in the R programming language that supports single- and multi-objective optimization with flexible parallelization options. The toolbox is designed to serve both practitioners applying standard Bayesian optimization and researchers building custom algorithms from its building blocks. Benchmarking against established optimizers like HEBO, SMAC3, Ax, and Optuna shows mlr3mbo performs competitively on standard optimization tasks.
mlr3mbo is a new software toolbox that provides a modular framework for Bayesian optimization in R, supporting both single- and multi-objective optimization problems. The system includes features for multi-point proposals, batch and asynchronous parallelization, and robust error handling. The developers conducted extensive empirical evaluations using the YAHPO Gym benchmark suite, performing coordinate descent searches to identify robust default configurations for numeric and mixed-hierarchical optimization scenarios. The toolbox was benchmarked against several state-of-the-art optimizers including HEBO, SMAC3, Ax, and Optuna, demonstrating competitive performance. The modular design allows both applied practitioners to use standard Bayesian optimization variants and researchers to construct custom algorithms from flexible building blocks.
What different sources said
- arXiv cs.LGCenter
mlr3mbo: Bayesian Optimization in R
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