[2305.06709] NUBO: A Transparent Python Package for Bayesian Optimization

Computer Science > Machine Learning arXiv:2305.06709 (cs) [Submitted on 11 May 2023 (v1), last revised 28 Feb 2026 (this version, v3)] 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 si...