  The performance of deep neural networks crucially depends on good
hyperparameter configurations. Bayesian optimization is a powerful framework
for optimizing the hyperparameters of DNNs. These methods need sufficient
evaluation data to approximate and minimize the validation error function of
hyperparameters. However, the expensive evaluation cost of DNNs leads to very
few evaluation data within a limited time, which greatly reduces the efficiency
of Bayesian optimization. Besides, the previous researches focus on using the
complete evaluation data to conduct Bayesian optimization, and ignore the
intermediate evaluation data generated by early stopping methods. To alleviate
the insufficient evaluation data problem, we propose a fast hyperparameter
optimization method, HOIST, that utilizes both the complete and intermediate
evaluation data to accelerate the hyperparameter optimization of DNNs.
Specifically, we train multiple basic surrogates to gather information from the
mixed evaluation data, and then combine all basic surrogates using weighted
bagging to provide an accurate ensemble surrogate. Our empirical studies show
that HOIST outperforms the state-of-the-art approaches on a wide range of DNNs,
including feed forward neural networks, convolutional neural networks,
recurrent neural networks, and variational autoencoder.
