Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds
We consider the optimization problem of an expensive-to-evaluate black-box function, in which we can obtain noisy function values in parallel. For this problem, parallel Bayesian optimization (PBO) is a promising approach, which aims to optimize with fewer function evaluations by selecting a diverse input set for parallel evaluation. However, existing PBO methods suffer from poor practical performance or lack theoretical guarantees. In this study, we propose a PBO method, called randomized kriging believer (KB), based on a well-known KB heuristic and inheriting the advantages of the original KB: low computational complexity, a simple implementation, versatility across various BO methods, ...
| Authors | Shuhei Sugiura, Ichiro Takeuchi, Shion Takeno |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 24 June 2026 |
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