{"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/controlled-random-search-improves-hyper","title":"Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization","arxiv_id":"1809.01712","date":"2018-09-05","proceeding":null,"authors":["Gowtham Muniraju","Bhavya Kailkhura","Jayaraman J. Thiagarajan","Peer-Timo Bremer","Cihan Tepedelenlioglu","Andreas Spanias"],"abstract":"Sampling one or more effective solutions from large search spaces is a\nrecurring idea in machine learning, and sequential optimization has become a\npopular solution. Typical examples include data summarization, sample mining\nfor predictive modeling and hyper-parameter optimization. Existing solutions\nattempt to adaptively trade-off between global exploration and local\nexploitation, wherein the initial exploratory sample is critical to their\nsuccess. While discrepancy-based samples have become the de facto approach for\nexploration, results from computer graphics suggest that coverage-based\ndesigns, e.g. Poisson disk sampling, can be a superior alternative. In order to\nsuccessfully adopt coverage-based sample designs to ML applications, which were\noriginally developed for 2-d image analysis, we propose fundamental advances by\nconstructing a parameterized family of designs with provably improved coverage\ncharacteristics, and by developing algorithms for effective sample synthesis.\nUsing experiments in sample mining and hyper-parameter optimization for\nsupervised learning, we show that our approach consistently outperforms\nexisting exploratory sampling methods in both blind exploration, and sequential\nsearch with Bayesian optimization.","url_abs":"http://arxiv.org/abs/1809.01712v3","url_pdf":"http://arxiv.org/pdf/1809.01712v3.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":"controlled-random-search-improves-hyper","repo_url":"https://github.com/gowthamasu/Coverage_based_sample_design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"data-summarization","task_name":"Data Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}