论文标题
贝叶斯优化的随机高斯工艺上限限制
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
论文作者
论文摘要
为了提高贝叶斯优化的性能，我们开发了修改的高斯工艺上限置信度（GP-UCB）的采集函数。这是通过从分布中抽样探索解释权衡参数来完成的。我们证明，这允许更改预期的权衡参数，以更好地解决问题，而不会损害该功能的贝叶斯遗憾。我们还提供结果表明，在一系列现实世界和合成问题中，我们的方法比GP-UCB更好的性能。
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.
