{"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/hebo-heteroscedastic-evolutionary-bayesian","title":"HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation","arxiv_id":"2012.03826","date":"2020-12-07","proceeding":null,"authors":["Alexander I. Cowen-Rivers","Wenlong Lyu","Rasul Tutunov","Zhi Wang","Antoine Grosnit","Ryan Rhys Griffiths","Alexandre Max Maraval","Hao Jianye","Jun Wang","Jan Peters","Haitham Bou Ammar"],"abstract":"In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers. Based on these findings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver (HEBO). HEBO performs non-linear input and output warping, admits exact marginal log-likelihood optimisation and is robust to the values of learned parameters. We demonstrate HEBO's empirical efficacy on the NeurIPS 2020 Black-Box Optimisation challenge, where HEBO placed first. Upon further analysis, we observe that HEBO significantly outperforms existing black-box optimisers on 108 machine learning hyperparameter tuning tasks comprising the Bayesmark benchmark. Our findings indicate that the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, multi-objective acquisition ensembles with Pareto front solutions improve queried configurations, and robust acquisition maximisers afford empirical advantages relative to their non-robust counterparts. We hope these findings may serve as guiding principles for practitioners of Bayesian optimisation. All code is made available at https://github.com/huawei-noah/HEBO.","url_abs":"https://arxiv.org/abs/2012.03826v6","url_pdf":"https://arxiv.org/pdf/2012.03826v6.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":"hebo-heteroscedastic-evolutionary-bayesian","repo_url":"https://github.com/huawei-noah/hebo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"hebo-heteroscedastic-evolutionary-bayesian","repo_url":"https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"hebo-heteroscedastic-evolutionary-bayesian","repo_url":"https://github.com/huawei-noah/noah-research/tree/master/HEBO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperparameter-optimization-on-bayesmark","task":"Hyperparameter Optimization","dataset":"Bayesmark","model":"HEBO","rank_in_archive_order":1,"of":2,"metrics":{"Mean":"100.117"},"uses_additional_data":false},{"leaderboard":"/sota/hyperparameter-optimization-on-bayesmark","task":"Hyperparameter Optimization","dataset":"Bayesmark","model":"TURBO","rank_in_archive_order":2,"of":2,"metrics":{"Mean":" 97.951"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2012.03826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}