单调形变用于加性及深度高斯过程
Monotonic warpings for additive and deep Gaussian processes
AI总结：
本文提出了一种高效的单调高斯过程，用于加性扩展和深度高斯过程中的非平稳替代建模，提升了模型性能。
摘要：
高斯过程（GPs）因其具有一定的解析可操作性而成为计算机实验的常用替代方法。但当响应面受约束，例如单调时，这种特性会失效。本文提供了一种针对单输入的mono-GP构造，即使计算非解析，其效率仍很高。关键成分包括参考过程的变换和椭圆切片采样。随后，我们展示了mono-GP可以以两种方式有效部署：一种是加性，将单调性扩展到更多输入；另一种是作为深度高斯过程中注入性潜在形变变量的先验，用于非单调、多输入非平稳替代建模。我们通过示例和基准测试展示了我们的方法在两类问题上的改进性能，优于当前最先进的方法。
英文摘要：
Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is constrained, say to be monotonic. Here, we provide a mono-GP construction for a single input that is highly efficient even though the calculations are non-analytic. Key ingredients include transformation of a reference process and elliptical slice sampling. We then show how mono-GP may be deployed effectively in two ways. One is additive, extending monotonicity to more inputs; the other is as a prior on injective latent warping variables in a deep Gaussian process for (non-monotonic, multi-input) non-stationary surrogate modeling. We provide illustrative and benchmarking examples throughout, showing that our methods yield improved performance over the state-of-the-art on examples from those two classes of problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
