Search Machine Learning Repository: @inproceedings{icml2014c2_gardner14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Bayesian Optimization with Inequality Constraints},
    Url = {http://jmlr.org/proceedings/papers/v32/gardner14.pdf},
    Abstract = {Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feasibility is just as expensive as evaluating the objective. Here we present constrained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions. We evaluate our method on simulated and real data, demonstrating that constrained Bayesian optimization can quickly find optimal and feasible points, even when small feasible regions cause standard methods to fail.},
    Author = {Jacob Gardner and Matt Kusner and Kilian Q. Weinberger and John Cunningham and Zhixiang Xu},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {937-945}
   }