{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gpflowopt-a-bayesian-optimization-library","title":"GPflowOpt: A Bayesian Optimization Library using TensorFlow","arxiv_id":"1711.03845","date":"2017-11-10","proceeding":null,"authors":["Nicolas Knudde","Joachim van der Herten","Tom Dhaene","Ivo Couckuyt"],"abstract":"A novel Python framework for Bayesian optimization known as GPflowOpt is\nintroduced. The package is based on the popular GPflow library for Gaussian\nprocesses, leveraging the benefits of TensorFlow including automatic\ndifferentiation, parallelization and GPU computations for Bayesian\noptimization. Design goals focus on a framework that is easy to extend with\ncustom acquisition functions and models. The framework is thoroughly tested and\nwell documented, and provides scalability. The current released version of\nGPflowOpt includes some standard single-objective acquisition functions, the\nstate-of-the-art max-value entropy search, as well as a Bayesian\nmulti-objective approach. Finally, it permits easy use of custom modeling\nstrategies implemented in GPflow.","url_abs":"http://arxiv.org/abs/1711.03845v1","url_pdf":"http://arxiv.org/pdf/1711.03845v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gpflowopt-a-bayesian-optimization-library","repo_url":"https://github.com/GPflow/GPflowOpt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.03845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.03845"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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