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Automl is the key technology for machine learning problem. Current state ofart hyperparameter optimization methods are based on traditional black-boxoptimization methods like SMBO (SMAC, TPE). The objective function of black-boxoptimization is non-smooth, or time-consuming to evaluate, or in some waynoisy. Recent years, many researchers offered the work about the properties ofhyperparameters. However, traditional hyperparameter optimization methods donot take those information into consideration. In this paper, we use gradientinformation and machine learning model analysis information to acceleratetraditional hyperparameter optimization methods SMBO. In our L2 normexperiments, our method yielded state-of-the-art performance, and in many casesoutperformed the previous best configuration approach.

Using Known Information to Accelerate HyperParameters Optimization Based on SMBO