# GP+: A Python Library for Kernel-based learning via Gaussian Processes

arXiv: https://arxiv.org/abs/2312.07694 · HTML: https://mlsanity.com/paper/2312.07694/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 17 of 17)

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- [Variational Bayesian surrogate modelling with application to robust design optimisation](https://mlsanity.com/paper/2404.14857/) (2024)
- [Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process](https://mlsanity.com/paper/2407.11268/) (2024)
- [Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning](https://mlsanity.com/paper/2606.17513/) (2026)
- [SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes](https://mlsanity.com/paper/2503.14785/) (2025)
- [Should We Simultaneously Calibrate Multiple Computer Models?](https://mlsanity.com/paper/2505.18176/) (2025)
- [MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting](https://mlsanity.com/paper/2602.20974/) (2026)
- [On the Uncertainty Quantification Ability of Tabular Foundation Models](https://mlsanity.com/paper/2606.01427/) (2026)
- [On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization](https://mlsanity.com/paper/2608.13793/) (2026)

## Abstract (arXiv)

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.