---
title: "SheffieldML/GPy: papers citing this GitHub repository"
description: "22 papers in the tracked journals link to the GitHub repository SheffieldML/GPy in their Crossref records, the first in 2018; most of them (5) in 2021."
url: https://scholar4ai.com/code/github-sheffieldml-gpy
markdown_url: https://scholar4ai.com/code/github-sheffieldml-gpy.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "22 papers in the tracked journals link to the GitHub repository SheffieldML/GPy in their Crossref records, the first in 2018; most of them (5) in 2021. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# SheffieldML/GPy: papers linking to it

Research papers in the 480 tracked journals that link to SheffieldML/GPy (https://github.com/SheffieldML/GPy), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 2,169 stars
- 573 forks
- Python
- license BSD-3-Clause
- created 2012
- https://github.com/SheffieldML/GPy

All repositories: https://scholar4ai.com/code.md

## Key findings

- 22 papers in the tracked journals link to the GitHub repository SheffieldML/GPy in their Crossref records, the first in 2018; most of them (5) in 2021.
- By category: AI (12), Operations Research (6) and Statistics (4); the journals with the most are Machine Learning (7), Statistics and Computing (4) and Optimization and Engineering (3).
- 2 of the papers build on one another: a later paper cites an earlier one that also links SheffieldML/GPy.

## By year

*Papers linking to SheffieldML/GPy by publication year*

| Year | Papers |
|---|---:|
| 2018 | 4 |
| 2019 | 1 |
| 2020 | 2 |
| 2021 | 5 |
| 2022 | 3 |
| 2023 | 3 |
| 2024 | 2 |
| 2025 | 2 |

## Journals

*Journals whose papers link to SheffieldML/GPy most*

| Journal | Papers |
|---|---:|
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 7 |
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 4 |
| [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | 3 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 2 |
| [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics](https://doi.org/10.1007/s10994-021-06019-1) (Machine Learning, 2021)
  - [A Unified Framework for Closed-Form Nonparametric Regression, Classification, Preference and Mixed Problems with Skew Gaussian Processes](https://doi.org/10.1007/s10994-021-06039-x) (Machine Learning, 2021) — cites the paper above

## Papers

*Papers linking to SheffieldML/GPy, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-06-24 | [Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics](https://doi.org/10.1007/s10994-021-06019-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Felix Berkenkamp, Andreas Krause, Angela P. Schoellig | 299 | cites it |
| 2022-12-13 | [Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming](https://doi.org/10.1007/s11222-022-10167-2) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Gabriel Riutort‐Mayol, Paul‐Christian Bürkner, Michael Riis Andersen et al. | 72 | cites it |
| 2021-08-20 | [Identifying material parameters in crystal plasticity by Bayesian optimization](https://doi.org/10.1007/s11081-021-09663-7) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Jannick Kuhn, Jonathan Spitz, Petra Sonnweber–Ribic et al. | 71 | cites it |
| 2018-03-01 | [Regression with small data sets: a case study using code surrogates in additive manufacturing](https://doi.org/10.1007/s10115-018-1174-1) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Chandrika Kamath, Ya Ju Fan | 40 | cites it |
| 2018-02-24 | [Reward estimation for dialogue policy optimisation](https://doi.org/10.1016/j.csl.2018.02.003) | [Computer Speech & Language](https://scholar4ai.com/journals/computer-speech-language.md) | Pei-Hao Su, Milica Gašić, S.J. Young | 18 | cites it |
| 2020-09-01 | [Skew Gaussian processes for classification](https://doi.org/10.1007/s10994-020-05906-3) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Alessio Benavoli, Dario Azzimonti, Dario Piga | 18 | cites it |
| 2020-10-16 | [A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression](https://doi.org/10.1007/s00521-020-05427-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Paulino José García Nieto, Esperanza García–Gonzalo, José P. Paredes–Sánchez et al. | 16 | cites it |
| 2022-01-19 | [A robust approach to warped Gaussian process-constrained optimization](https://doi.org/10.1007/s10107-021-01762-8) | [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | Johannes Wiebe, I. Cecílio, J. Dunlop et al. | 15 | cites it |
| 2019-02-21 | [Antithetic and Monte Carlo kernel estimators for partial rankings](https://doi.org/10.1007/s11222-019-09859-z) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | María Lomelí, Mark Rowland, Arthur Gretton et al. | 13 | cites it |
| 2022-02-19 | [Bayesian mixture of gaussian processes for data association problem](https://doi.org/10.1016/j.patcog.2022.108592) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Younghwan Jeon, Ganguk Hwang | 13 | cites it |
| 2023-01-25 | [Correlated product of experts for sparse Gaussian process regression](https://doi.org/10.1007/s10994-022-06297-3) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Manuel Schürch, Dario Azzimonti, Alessio Benavoli et al. | 11 | cites it |
| 2021-12-21 | [ROmodel: modeling robust optimization problems in Pyomo](https://doi.org/10.1007/s11081-021-09703-2) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Johannes Wiebe, Ruth Misener | 9 | cites it |
| 2021-09-13 | [A Unified Framework for Closed-Form Nonparametric Regression, Classification, Preference and Mixed Problems with Skew Gaussian Processes](https://doi.org/10.1007/s10994-021-06039-x) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Alessio Benavoli, Dario Azzimonti, Dario Piga | 8 | cites it |
| 2021-04-20 | [Bayesian optimization for a multiple-component system with target values](https://doi.org/10.1016/j.cie.2021.107310) | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | Jihwan Jeong, Hayong Shin | 7 | cites it |
| 2024-01-08 | [Fast deep mixtures of Gaussian process experts](https://doi.org/10.1007/s10994-023-06491-x) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Clement Etienam, Kody J. H. Law, Sara Wade et al. | 7 | cites it |
| 2018-07-06 | [Stochastic variational hierarchical mixture of sparse Gaussian processes for regression](https://doi.org/10.1007/s10994-018-5721-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Thi Nhat Anh Nguyen, Abdesselam Bouzerdoum, Son Lam Phung | 5 | cites it |
| 2018-07-20 | [GoGP: scalable geometric-based Gaussian process for online regression](https://doi.org/10.1007/s10115-018-1239-1) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Trung Le, Khanh Nguyen, Vu Nguyen et al. | 4 | cites it |
| 2023-05-29 | [A similarity-based Bayesian mixture-of-experts model](https://doi.org/10.1007/s11222-023-10238-y) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Tianfang Zhang, Rasmus Bokrantz, Jimmy Olsson | 4 | cites it |
| 2025-01-20 | [A general-purpose approach to multi-agent Bayesian optimization across decomposition methods](https://doi.org/10.1007/s11081-024-09953-w) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Dinesh Krishnamoorthy | 3 | cites it |
| 2024-03-11 | [Lower Bounds on the Noiseless Worst-Case Complexity of Efficient Global Optimization](https://doi.org/10.1007/s10957-024-02399-1) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Wenjie Xu, Yuning Jiang, Emilio Tanowe Maddalena et al. | 2 | cites it |
| 2023-11-03 | [Likelihood-free inference in state-space models with unknown dynamics](https://doi.org/10.1007/s11222-023-10339-8) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Alexander Aushev, Thong Anh Tran, Henri Pesonen et al. | 1 | cites it |
| 2025-11-20 | [Beyond What’s Normal: Bimodal and Heaviside Alternatives to Gaussian Process Regression](https://doi.org/10.1007/s10994-025-06934-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Thomas M. Whitehead | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). SheffieldML/GPy: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-sheffieldml-gpy

```bibtex
@misc{scholar4ai-code-github-sheffieldml-gpy,
  author = {{Scholar4AI}},
  title = {SheffieldML/GPy: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-sheffieldml-gpy}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).