Bayesian Optimization using Pseudo-Points
This work addresses optimization efficiency for applications like parameter tuning and robotics, but it appears incremental as it builds on existing acquisition functions with pseudo-points.
The paper tackles the problem of expensive black-box optimization in Bayesian optimization by proposing a framework that uses pseudo-points to improve Gaussian process models, resulting in proven regret bounds and experimental advantages across synthetic and real-world tasks.
Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, 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.