CobBO: Coordinate Backoff Bayesian Optimization

Bayesian optimization is a popular method for optimizing expensive black-box functions. The objective functions of hard real world problems are oftentimes characterized by a fluctuated landscape of many local optima. Bayesian optimization risks in over-exploiting such traps, remaining with insufficient query budget for exploring the global landscape. We introduce Coordinate Backoff Bayesian Optimization (CobBO) to alleviate those challenges. CobBO captures a smooth approximation of the global landscape by interpolating the values of queried points projected to randomly selected promising subspaces. Thus also a smaller query budget is required for the Gaussian process regressions applied over the lower dimensional subspaces. This approach can be viewed as a variant of coordinate ascent, tailored for Bayesian optimization, using a stopping rule for backing off from a certain subspace and switching to another coordinate subset. Extensive evaluations show that CobBO finds solutions comparable to or better than other state-of-the-art methods for dimensions ranging from tens to hundreds, while reducing the trial complexity.