Saurabh Paul, Christos Boutsidis, et al.
JMLR
Bayesian Optimization (BO) in its classical form is cost-unaware. However, many real-world problems are resource-constrained and hence incur a cost whenever such resources are needed, such as when a new setup is used. We are then looking at adapted cost-aware solution methods that are improving the performance of BO over cost-constrained problems. We find that parameter-free algorithms can yield comparable results to fine-tuned algorithms used in constrained optimization
Saurabh Paul, Christos Boutsidis, et al.
JMLR
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Joxan Jaffar
Journal of the ACM
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM