{"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/achieving-the-time-of-1-nn-but-the-accuracy","title":"Achieving the time of $1$-NN, but the accuracy of $k$-NN","arxiv_id":"1712.02369","date":"2017-12-06","proceeding":null,"authors":["Lirong Xue","Samory Kpotufe"],"abstract":"We propose a simple approach which, given distributed computing resources,\ncan nearly achieve the accuracy of $k$-NN prediction, while matching (or\nimproving) the faster prediction time of $1$-NN. The approach consists of\naggregating denoised $1$-NN predictors over a small number of distributed\nsubsamples. We show, both theoretically and experimentally, that small\nsubsample sizes suffice to attain similar performance as $k$-NN, without\nsacrificing the computational efficiency of $1$-NN.","url_abs":"http://arxiv.org/abs/1712.02369v2","url_pdf":"http://arxiv.org/pdf/1712.02369v2.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":"achieving-the-time-of-1-nn-but-the-accuracy","repo_url":"https://github.com/lirongx/SubNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}