Bayesian Optimization using Pseudo-Points

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, and robotics. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing an acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i. e. , data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i. e. , upper confidence bound (UCB), we prove that the cumulative regret can be generally upper bounded. Experiments using UCB and other acquisition functions, i. e. , probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.

Authors

• Chao Qian School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
• Hang Xiong
• Ke Xue National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Keywords

• Heuristic Search and Game Playing: Heuristic Search
• Heuristic Search and Game Playing: Heuristic Search and Machine Learning
• Machine Learning: Bayesian Optimization