提出一种可理论保证的并行贝叶斯优化方法，提升效率与稳定性。
Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds
- 基于随机化克里金信念者启发式，实现高效并行采样
- 在真实数据模拟器上验证，显著降低累积后悔值
- 适合需快速收敛的高成本函数优化场景
针对评估代价高昂的黑箱函数优化问题，允许并行获取带噪函数值。现有并行贝叶斯优化方法或实践性能差，或缺乏理论保障。本文提出一种新方法——随机化克里金信念者（Randomized Kriging Believer, KB），基于经典KB启发式，继承其低计算复杂度、实现简单、兼容多种贝叶斯优化框架及支持异步并行等优点。同时，证明了该方法具备贝叶斯期望后悔率理论保证。实验结果表明，该方法在真实数据模拟器上表现优异，显著优于基线方法。
原文摘要 · Abstract (English)
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, and applicability to asynchronous parallelization. Furthermore, we show that our randomized KB achieves Bayesian expected regret guarantees. We demonstrate the effectiveness of the proposed method through experiments, including those on real-data emulators.
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