{"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/parallel-gaussian-process-regression-with-low","title":"Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations","arxiv_id":"1305.5826","date":"2013-05-24","proceeding":null,"authors":["Jie Chen","Nannan Cao","Kian Hsiang Low","Ruofei Ouyang","Colin Keng-Yan Tan","Patrick Jaillet"],"abstract":"Gaussian processes (GP) are Bayesian non-parametric models that are widely\nused for probabilistic regression. Unfortunately, it cannot scale well with\nlarge data nor perform real-time predictions due to its cubic time cost in the\ndata size. This paper presents two parallel GP regression methods that exploit\nlow-rank covariance matrix approximations for distributing the computational\nload among parallel machines to achieve time efficiency and scalability. We\ntheoretically guarantee the predictive performances of our proposed parallel\nGPs to be equivalent to that of some centralized approximate GP regression\nmethods: The computation of their centralized counterparts can be distributed\namong parallel machines, hence achieving greater time efficiency and\nscalability. We analytically compare the properties of our parallel GPs such as\ntime, space, and communication complexity. Empirical evaluation on two\nreal-world datasets in a cluster of 20 computing nodes shows that our parallel\nGPs are significantly more time-efficient and scalable than their centralized\ncounterparts and exact/full GP while achieving predictive performances\ncomparable to full GP.","url_abs":"http://arxiv.org/abs/1305.5826v1","url_pdf":"http://arxiv.org/pdf/1305.5826v1.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":"parallel-gaussian-process-regression-with-low","repo_url":"https://github.com/arikcj/pgpr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1305.5826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}