Learning Neural Surrogate Model for Warm-Starting Bayesian Optimization

Bayesian optimization is an effective tool to optimize black-box functions and popular for hyper-parameter tuning in machine learning. Traditional Bayesian optimization methods are based on Gaussian process (GP), relying on a GP-based surrogate model for sampling points of the function of interest. In this work, we consider transferring knowledge from related problems to target problem by learning an initial surrogate model for warm-starting Bayesian optimization. We propose a neural network-based surrogate model to estimate the function mean value in GP. Then we design a novel weighted Reptile algorithm with sampling strategy to learn an initial surrogate model from meta train set. The initial surrogate model is learned to be able to well adapt to new tasks. Extensive experiments show that this warm-starting technique enables us to find better minimizer or hyper-parameters than traditional GP and previous warm-starting methods.