In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.

翻译：为提升贝叶斯优化性能，我们提出一种改进的高斯过程上置信界（GP-UCB）采集函数。该方法通过对探索-利用权衡参数进行分布采样实现。我们证明，此方法可在不破坏函数贝叶斯遗憾界的前提下，调整期望权衡参数以更好地适应问题特性。实验结果表明，在多种真实场景与合成问题中，本方法均取得了优于标准GP-UCB的性能表现。