Robust Entropy Search for Safe Efficient Bayesian Optimization
<|reference_start|>Robust Entropy Search for Safe Efficient Bayesian Optimization: The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even adversely perturbed at the time of application. To this end, we develop an efficient information-based acquisition function that we call Robust Entropy Search (RES). We empirically demonstrate its benefits in experiments on synthetic and real-life data. The results showthat RES reliably finds robust optima, outperforming state-of-the-art algorithms.<|reference_end|>
@article{weichert2024robust,
    title={Robust Entropy Search for Safe Efficient Bayesian Optimization},
    author={Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link,
  Gunar Ernis},
    journal={arXiv preprint arXiv:2405.19059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19059},
    primaryClass={cs.LG stat.ML}
}