Bayesian Optimisation with Formal Guarantees
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Abstract (arXiv)
Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even though they do have explicit representations, standard optimization techniques fail to provide validated solutions and correctness guarantees for them. In this paper we present a combination of Bayesian optimisation and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.
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5 of 15 references.
References · 5
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2018Adversarially Robust Optimization with Gaussian Processescited by 62
- 2018Robustness Guarantees for Bayesian Inference with Gaussian Processescited by 23
- 2019A CDCL-style calculus for solving non-linear constraintscited by 3
- 2021The ksmt calculus is a δ-complete decision procedure for non-linear constraintscited by 2
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