{"ID":2827096,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17569","arxiv_id":"2512.17569","title":"Bayesian Optimisation: Which Constraints Matter?","abstract":"Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \\emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated independently. In particular, our methods aim to take into account that often only a handful of the constraints may be binding at the optimum, and hence we should evaluate only relevant constraints when trying to optimise a function. We empirically benchmark these methods against existing methods and demonstrate their superiority over the state-of-the-art.","short_abstract":"Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \\emph{decoupled} black-box constraints, in which subsets of the objective an...","url_abs":"https://arxiv.org/abs/2512.17569","url_pdf":"https://arxiv.org/pdf/2512.17569v1","authors":"[\"Xietao Wang Lin\",\"Juan Ungredda\",\"Max Butler\",\"James Town\",\"Alma Rahat\",\"Hemant Singh\",\"Juergen Branke\"]","published":"2025-12-19T13:35:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"math.OC\"]","methods":"[]","has_code":false}