Hyper-parameter selection is a crucial yet difficult issue in machine learning. For this problem, derivative-free optimization has being playing an irreplaceable role. However, derivative-free optimization commonly requires a lot of hyperparameter samples, while each sample could have a high cost for hyper-parameter selection due to the costly evaluation of a learning model. To tackle this issue, in this paper, we propose an experienced optimization approach, i.e., learning how to optimize better from a set of historical optimization processes. From the historical optimization processes on previous datasets, a directional model is trained to predict the direction of the next good hyper-parameter. The directional model is then reused to guide the optimization on learning new datasets. We implement this mechanism within a state-of-the-art derivative-free optimization method SRACOS, and conduct experiments on learning the hyper-parameters of heterogeneous ensembles and neural network architectures. Experimental results verify that the proposed approach can significantly improve the learning accuracy within a limited hyper-parameter sample budget.
REF proposed an experienced directional model which was learned from the previous optimization processes to predict the optimization direction.
Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search
IJCAI
Computer Science