  Automl is the key technology for machine learning problem. Current state of
art hyperparameter optimization methods are based on traditional black-box
optimization methods like SMBO (SMAC, TPE). The objective function of black-box
optimization is non-smooth, or time-consuming to evaluate, or in some way
noisy. Recent years, many researchers offered the work about the properties of
hyperparameters. However, traditional hyperparameter optimization methods do
not take those information into consideration. In this paper, we use gradient
information and machine learning model analysis information to accelerate
traditional hyperparameter optimization methods SMBO. In our L2 norm
experiments, our method yielded state-of-the-art performance, and in many cases
outperformed the previous best configuration approach.
