{"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/practical-coreset-constructions-for-machine","title":"Practical Coreset Constructions for Machine Learning","arxiv_id":"1703.06476","date":"2017-03-19","proceeding":null,"authors":["Olivier Bachem","Mario Lucic","Andreas Krause"],"abstract":"We investigate coresets - succinct, small summaries of large data sets - so\nthat solutions found on the summary are provably competitive with solution\nfound on the full data set. We provide an overview over the state-of-the-art in\ncoreset construction for machine learning. In Section 2, we present both the\nintuition behind and a theoretically sound framework to construct coresets for\ngeneral problems and apply it to $k$-means clustering. In Section 3 we\nsummarize existing coreset construction algorithms for a variety of machine\nlearning problems such as maximum likelihood estimation of mixture models,\nBayesian non-parametric models, principal component analysis, regression and\ngeneral empirical risk minimization.","url_abs":"http://arxiv.org/abs/1703.06476v2","url_pdf":"http://arxiv.org/pdf/1703.06476v2.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":"practical-coreset-constructions-for-machine","repo_url":"https://github.com/boniface316/vqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"practical-coreset-constructions-for-machine","repo_url":"https://github.com/patel-zeel/coreset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"practical-coreset-constructions-for-machine","repo_url":"https://github.com/teaguetomesh/coresets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.06476","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}