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 show that RES reliably finds robust optima, outperforming state-of-the-art algorithms.

  • Published in:
    Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence
  • Type:
    Inproceedings
  • Authors:
    Weichert, Dorina; Kister, Alexander; Houben, Sebastian; Link, Patrick; Ernis, Gunar
  • Year:
    2024

Citation information

Weichert, Dorina; Kister, Alexander; Houben, Sebastian; Link, Patrick; Ernis, Gunar: Robust Entropy Search for Safe Efficient Bayesian Optimization, Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, 2024, 244, 3711--3729, PMLR, https://proceedings.mlr.press/v244/weichert24a.html, Weichert.etal.2024a,

Associated Lamarr Researchers

lamarr institute person Weichert Dorina - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Dorina Weichert

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Portrait of Sebastian Houben.

Dr. Sebastian Houben

Author to the profile