  In this paper we introduce GP+, an open-source library for kernel-based
learning via Gaussian processes (GPs) which are powerful statistical models
that are completely characterized by their parametric covariance and mean
functions. GP+ is built on PyTorch and provides a user-friendly and
object-oriented tool for probabilistic learning and inference. As we
demonstrate with a host of examples, GP+ has a few unique advantages over other
GP modeling libraries. We achieve these advantages primarily by integrating
nonlinear manifold learning techniques with GPs' covariance and mean functions.
As part of introducing GP+, in this paper we also make methodological
contributions that (1) enable probabilistic data fusion and inverse parameter
estimation, and (2) equip GPs with parsimonious parametric mean functions which
span mixed feature spaces that have both categorical and quantitative
variables. We demonstrate the impact of these contributions in the context of
Bayesian optimization, multi-fidelity modeling, sensitivity analysis, and
calibration of computer models.
