Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

@InProceedings{pmlr-v97-alvi19a,
  title = 	 {Asynchronous Batch {B}ayesian Optimisation with Improved Local Penalisation},
  author =       {Alvi, Ahsan and Ru, Binxin and Calliess, Jan-Peter and Roberts, Stephen and Osborne, Michael A.},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {253--262},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/alvi19a/alvi19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/alvi19a.html},
  abstract = 	 {Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on K Workers (PLAyBOOK), for asynchronous parallel BO. We demonstrate empirically the efficacy of PLAyBOOK and its variants on synthetic tasks and a real-world problem. We undertake a comparison between synchronous and asynchronous BO, and show that asynchronous BO often outperforms synchronous batch BO in both wall-clock time and sample efficiency.}
}