Asynchronous \epsilon-Greedy Bayesian Optimisation

Autor:	De Ath, George, Everson, Richard M., Fieldsend, Jonathan E.
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource utilisation. To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous $\epsilon$-Greedy Global Search) that combines greedy search, exploiting the surrogate's mean prediction, with Thompson sampling and random selection from the approximate Pareto set describing the trade-off between exploitation (surrogate mean prediction) and exploration (surrogate posterior variance). We demonstrate empirically the efficacy of AEGiS on synthetic benchmark problems, meta-surrogate hyperparameter tuning problems and real-world problems, showing that AEGiS generally outperforms existing methods for asynchronous BO. When a single worker is available performance is no worse than BO using expected improvement. Comment: Accepted for the 37th conference on Uncertainty in Artificial Intelligence (UAI 2021). 11 pages (main paper) + 22 pages (supplementary material)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.07615 Zobrazit plný text záznamu View this record from Arxiv