Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, approximations are typically needed, which typically do require tuning. We argue that this requirement for tuning complicates evaluation, which has led to a lack of a clear recommendations on which method should be used in which situation. To address this, we make recommendations for comparing GP approximations based on a specification of what a user should expect from a method. In addition, we develop a training procedure for the variational method of Titsias [2009] that leaves no choices to the user, and show that this is a strong baseline that meets our specification. We conclude that benchmarking according to our suggestions gives a clearer view of the current state of the field, and uncovers problems that are still open that future papers should address.

翻译：高斯过程（GPs）是机器学习工具箱中成熟且广泛使用的组件。其理想特性之一在于自动超参数选择，使得无需用户干预即可进行训练。然而，在实际场景中，通常需要近似方法，而这类方法往往需要调参。我们认为，这种调参需求使得评估变得复杂，导致在何种情况下应使用何种方法缺乏明确建议。为解决这一问题，我们基于用户对方法应达成的期望规格，提出了比较GP近似的建议。此外，我们针对Titsias [2009] 的变分方法开发了一套无需用户干预的训练流程，并证明该方法是一个满足规格的强基线。结论表明，按照我们的建议进行基准测试能更清晰地揭示该领域当前的研究现状，并揭示未来论文仍需解决的开放性问题。