[2305.06709] NUBO: A Transparent Python Package for Bayesian Optimization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2305.06709 (cs) [Submitted on 11 May 2023 ( v1 ), last revised 28 Apr 2026 (this version, v4)] Title: NUBO: A Transparent Python Package for Bayesian Optimization Authors: Mike Diessner , Kevin J. Wilson , Richard D. Whalley View a PDF of the paper titled NUBO: A Transparent Python Package for Bayesian Optimization, by Mike Diessner and 2 other authors View PDF HTML (experimental) Abstract: NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators. Bayesian optimization is a costefficient optimization strategy that uses surrogate modelling via Gaussian processes to represent an objective function and acquisition functions to guide the selection of candidate points to approximate the global optimum of the objective function. NUBO itself focuses on transparency and user experience to make Bayesian optimization easily accessible to researchers from all disciplines. Clean and understandable code, precise references, and thorough documentation ensure transparency, while user experience is ensured by a modular and flexible design, easy-to-write syntax, and careful selection of Bayesian optimization algorithms. NUBO allows users to tailor Bayesian optimization to their specific problem by writing the optimization loop themselves using the provided building blocks. It supports sequential single-point, parallel multi-point, and asynchronous optimization of bounded, constrained, and/or mixed (discrete and continuous) parameter input spaces. Only algorithms and methods that are extensively tested and validated to perform well are included in NUBO. This ensures that the package remains compact and does not overwhelm the user with an unnecessarily large number of options. The package is written in Python but does not require expert knowledge of Python to optimize your simulators and experiments. NUBO is distributed as open-source software under the BSD 3-Clause license. Subjects: Machine Learning (cs.LG) ; Mathematical Software (cs.MS); Machine Learning (stat.ML) Cite as: arXiv:2305.06709 [cs.LG] (or arXiv:2305.06709v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2305.06709 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of Statistical Software, 114(1), 1-28 (2025) Related DOI : https://doi.org/10.18637/jss.v114.i01 Focus to learn more DOI(s) linking to related resources Submission history From: Mike Diessner [ view email ] [v1] Thu, 11 May 2023 10:34:27 UTC (563 KB) [v2] Mon, 3 Jun 2024 07:52:21 UTC (539 KB) [v3] Sat, 28 Feb 2026 19:56:35 UTC (536 KB) [v4] Tue, 28 Apr 2026 07:08:12 UTC (536 KB) Full-text links: Access Paper: View a PDF of the paper titled NUBO: A Transparent Python Package for Bayesian Optimization, by Mike Diessner and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2023-05 Change to browse by: cs cs.MS stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from