Batched Energy-Entropy acquisition for Bayesian Optimization
Keywords: bayesian optimization, gaussian processes, acquisition, batch, parallel
TL;DR: A novel acquisition function for batched Bayesian optimization with Gaussian processes that trades off exploration and exploitation with a controllable parameter.
Abstract: Bayesian optimization (BO) is an attractive machine learning framework for performing sample-efficient global optimization of black-box functions. The optimization process is guided by an acquisition function that selects points to acquire in each round of BO. In batched BO, when multiple points are acquired in parallel, commonly used acquisition functions are often high-dimensional and intractable, leading to the use of sampling-based alternatives. We propose a statistical physics inspired acquisition function that can natively handle batches. Batched Energy-Entropy acquisition for BO (BEEBO) enables tight control of the explore-exploit trade-off of the optimization process and generalizes to heteroskedastic black-box problems. We demonstrate the applicability of BEEBO on a range of problems, showing competitive performance to existing acquisition functions.
Primary Area: Active learning
Submission Number: 16846
Loading