A flexible transfer learning framework for Bayesian optimization with convergence guarantee

Abstract

Experimental optimization is prevalent in many areas of artificial intelligence including machine learning. Conventional methods like grid search and random search can be computationally demanding. Over the recent years, Bayesian optimization has emerged as an efficient technique for global optimization of black-box functions. However, a generic Bayesian optimization algorithm suffers from a “cold start” problem. It may struggle to find promising locations in the initial stages. We propose a novel transfer learning method for Bayesian optimization where we leverage the knowledge from an already completed source optimization task for the optimization of a target task. Assuming both the source and target functions lie in some proximity to each other, we model source data as noisy observations of the target function. The level of noise models the proximity or relatedness between the tasks. We provide a mechanism to compute the noise level from the data to automatically adjust for different relatedness between the source and target tasks. We then analyse the convergence properties of the proposed method using two popular acquisition functions. Our theoretical results show that the proposed method converges faster than a generic no-transfer Bayesian optimization. We demonstrate the effectiveness of our method empirically on the tasks of tuning the hyperparameters of three different machine learning algorithms. In all the experiments, our method outperforms state-of-the-art transfer learning and no-transfer Bayesian optimization methods.