Random Postprocessing for Combinatorial Bayesian Optimization
This addresses the slow convergence issue in Bayesian optimization for high-dimensional problems, offering a simple strategy, though it appears incremental as it builds on existing methods.
The paper tackles the problem of slow convergence in Bayesian optimization due to repeated sampling by introducing a postprocessing method that prohibits duplicate samples, finding it significantly reduces the number of sequential steps needed to find the global optimum, especially with maximum a posterior estimation acquisition functions.
Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in many steps to find the global optimum. Here, we numerically study the effect of a postprocessing method on Bayesian optimization that strictly prohibits duplicated samples in the dataset. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posterior estimation. Our results provide a simple but general strategy to solve the slow convergence of Bayesian optimization for high-dimensional problems.