论文标题
异步ε-greedy贝叶斯优化
Asynchronous ε-Greedy Bayesian Optimisation
论文作者
论文摘要
批处理贝叶斯优化（BO）是一种成功优化昂贵的黑盒功能的技术。异步BO可以在另一个完成后立即开始新的评估，从而最大程度地利用资源，从而减少壁挂时间。 To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous $ε$-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).我们从经验上证明了宙斯盾对合成基准问题，元流式高参数调谐问题和现实世界中的问题的疗效，这表明宙斯盾通常比异步BO的现有方法胜过现有的方法。当单个工人可用时，绩效并不比BO使用预期的改进要差。
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 $ε$-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.
