  Bayesian Optimization (BO) is a common solution to search optimal
hyperparameters based on sample observations of a machine learning model.
Existing BO algorithms could converge slowly even collapse when the potential
observation noise misdirects the optimization. In this paper, we propose a
novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to
solve the problem. We first propose a neighbor-based regularization to smooth
each sample observation, which could reduce the observation noise efficiently
without any extra training cost. Since the neighbor regularization highly
depends on the sample density of a neighbor area, we further design a
density-based acquisition function to adjust the acquisition reward and obtain
more stable statistics. In addition, we design a adjustment mechanism to ensure
the framework maintains a reasonable regularization strength and density reward
conditioned on remaining computation resources. We conduct experiments on the
bayesmark benchmark and important computer vision benchmarks such as ImageNet
and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it
consistently outperforms other state-of-the-art methods.
