The huge availability of data in the last decade has raised the opportunity for the better use of data in decision-making processes. The idea of using the existing data to achieve a more coherent reality solution has led to a branch of optimization called data-driven optimization. On the one hand, the presence of uncertain variables in these datasets makes it crucial to design robust optimization methods in this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by the above points, in this paper a Gaussian process is used in a Bayesian optimization framework to design a method that is consistent with the data in a predefined confidence level. The advantage of the proposed method is that it is computationally tractable in addition to being robust and independent of the objective function’s form. As one of the applications of the proposed algorithm, hyper-parameter optimization for deep learning is investigated. The proposed method can help find the optimal hyper-parameters that are robust with respect to noise.

过去十年中大量可用的数据为在决策过程中更好地利用数据提供了机会。使用现有数据来实现更一致的现实解决方案的想法导致了一个称为数据驱动优化的优化分支。一方面，这些数据集中存在的不确定变量使得在该领域设计稳健的优化方法变得至关重要。另一方面，在许多现实世界的问题中，目标函数的封闭形式是不可用的，并且元-基于模型的框架是必要的。受以上几点启发，本文在贝叶斯优化框架中使用高斯过程来设计一种在预定义置信水平下与数据一致的方法。所提出的方法的优点是除了鲁棒性和独立于目标函数的形式之外，它在计算上易于处理。作为所提出算法的应用之一，研究了深度学习的超参数优化。所提出的方法可以帮助找到对噪声具有鲁棒性的最佳超参数。