An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

Yimin Huang; Yujun Li; Hanrong Ye; Zhenguo Li; Zhihua Zhang

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arXiv · 2007.05670

An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

Yimin Huang·Yujun Li·Hanrong Ye·Zhenguo Li·Zhihua Zhang

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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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BibTeX RIS

Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang. 2020-07-11. An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization. https://arxiv.org/abs/2007.05670

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