An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

• URL: http://arxiv.org/abs/2007.05670v2
• Date: Wed, 16 Dec 2020 10:28:43 GMT
• Title: An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization
• Authors: Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang
• Abstract summary: We propose an efficient and robust bandit-based algorithm called Sub-Sampling (SS) in the scenario of hyper parameter search evaluation. We also develop a novel hyper parameter optimization algorithm called BOSS. Empirical studies validate our theoretical arguments of SS and demonstrate the superior performance of BOSS on a number of applications.
• Score: 48.5614138038673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparameter search space. In this paper, we propose an efficient and robust bandit-based algorithm called Sub-Sampling (SS) in the scenario of hyperparameter search evaluation. It evaluates the potential of hyperparameters by the sub-samples of observations and is theoretically proved to be optimal under the criterion of cumulative regret. We further combine SS with Bayesian Optimization and develop a novel hyperparameter optimization algorithm called BOSS. Empirical studies validate our theoretical arguments of SS and demonstrate the superior performance of BOSS on a number of applications, including Neural Architecture Search (NAS), Data Augmentation (DA), Object Detection (OD), and Reinforcement Learning (RL).

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