{"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/multiresolution-kernel-approximation-for","title":"Multiresolution Kernel Approximation for Gaussian Process Regression","arxiv_id":"1708.02183","date":"2017-08-07","proceeding":"NeurIPS 2017 12","authors":["Yi Ding","Risi Kondor","Jonathan Eskreis-Winkler"],"abstract":"Gaussian process regression generally does not scale to beyond a few\nthousands data points without applying some sort of kernel approximation\nmethod. Most approximations focus on the high eigenvalue part of the spectrum\nof the kernel matrix, $K$, which leads to bad performance when the length scale\nof the kernel is small. In this paper we introduce Multiresolution Kernel\nApproximation (MKA), the first true broad bandwidth kernel approximation\nalgorithm. Important points about MKA are that it is memory efficient, and it\nis a direct method, which means that it also makes it easy to approximate\n$K^{-1}$ and $\\mathop{\\textrm{det}}(K)$.","url_abs":"http://arxiv.org/abs/1708.02183v3","url_pdf":"http://arxiv.org/pdf/1708.02183v3.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":"multiresolution-kernel-approximation-for","repo_url":"https://github.com/niklasschmitz/MultiresolutionKernelApproximationSlides","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.02183","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}