Name
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 number of function evaluations. |
Year | Venue | Field |
|---|---|---|
2019 | arXiv: Machine Learning | Asynchronous communication,Computer science,Artificial intelligence,Machine learning,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1901.10452 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahsan S. Alvi | 1 | 0 | 0.68 |
| Bin Xin Ru | 2 | 1 | 4.42 |
| Jan-P. Calliess | 3 | 44 | 9.37 |
| stephen j roberts | 4 | 1244 | 174.70 |
| Michael Osborne | 5 | 250 | 33.49 |