{"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/fast-bayesian-optimization-of-machine","title":"Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets","arxiv_id":"1605.07079","date":"2016-05-23","proceeding":null,"authors":["Aaron Klein","Stefan Falkner","Simon Bartels","Philipp Hennig","Frank Hutter"],"abstract":"Bayesian optimization has become a successful tool for hyperparameter\noptimization of machine learning algorithms, such as support vector machines or\ndeep neural networks. Despite its success, for large datasets, training and\nvalidating a single configuration often takes hours, days, or even weeks, which\nlimits the achievable performance. To accelerate hyperparameter optimization,\nwe propose a generative model for the validation error as a function of\ntraining set size, which is learned during the optimization process and allows\nexploration of preliminary configurations on small subsets, by extrapolating to\nthe full dataset. We construct a Bayesian optimization procedure, dubbed\nFabolas, which models loss and training time as a function of dataset size and\nautomatically trades off high information gain about the global optimum against\ncomputational cost. Experiments optimizing support vector machines and deep\nneural networks show that Fabolas often finds high-quality solutions 10 to 100\ntimes faster than other state-of-the-art Bayesian optimization methods or the\nrecently proposed bandit strategy Hyperband.","url_abs":"http://arxiv.org/abs/1605.07079v2","url_pdf":"http://arxiv.org/pdf/1605.07079v2.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":"fast-bayesian-optimization-of-machine","repo_url":"https://github.com/automl/RoBO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1605.07079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.07079"}},"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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