论文标题
嘈杂的输入熵搜索有效的稳健贝叶斯优化
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
论文作者
论文摘要
我们考虑在良好的贝叶斯优化（BO）框架内的强大优化问题。尽管BO对目标函数的嘈杂评估本质上是强大的，但标准方法并未考虑输入参数的不确定性。在本文中，我们提出了嘈杂的输入熵搜索（NES），这是一种新型的信息理论采集函数，旨在找到针对输入和测量噪声问题的强大优化。 NES基于关键见解，即在许多情况下，可靠的目标可以建模为高斯过程，但是，无法直接观察到它。我们从优化文献和工程学中评估了NES的几个基准问题。结果表明，NES可靠地找到了强大的Optima，从所有基准的文献中都优于现有方法。
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework. While BO is intrinsically robust to noisy evaluations of the objective function, standard approaches do not consider the case of uncertainty about the input parameters. In this paper, we propose Noisy-Input Entropy Search (NES), a novel information-theoretic acquisition function that is designed to find robust optima for problems with both input and measurement noise. NES is based on the key insight that the robust objective in many cases can be modeled as a Gaussian process, however, it cannot be observed directly. We evaluate NES on several benchmark problems from the optimization literature and from engineering. The results show that NES reliably finds robust optima, outperforming existing methods from the literature on all benchmarks.
