{"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/large-linear-multi-output-gaussian-process","title":"Large Linear Multi-output Gaussian Process Learning","arxiv_id":"1705.10813","date":"2017-05-30","proceeding":null,"authors":["Vladimir Feinberg","Li-Fang Cheng","Kai Li","Barbara E. Engelhardt"],"abstract":"Gaussian processes (GPs), or distributions over arbitrary functions in a\ncontinuous domain, can be generalized to the multi-output case: a linear model\nof coregionalization (LMC) is one approach. LMCs estimate and exploit\ncorrelations across the multiple outputs. While model estimation can be\nperformed efficiently for single-output GPs, these assume stationarity, but in\nthe multi-output case the cross-covariance interaction is not stationary. We\npropose Large Linear GP (LLGP), which circumvents the need for stationarity by\ninducing structure in the LMC kernel through a common grid of inputs shared\nbetween outputs, enabling optimization of GP hyperparameters for\nmulti-dimensional outputs and low-dimensional inputs. When applied to synthetic\ntwo-dimensional and real time series data, we find our theoretical improvement\nrelative to the current solutions for multi-output GPs is realized with LLGP\nreducing training time while improving or maintaining predictive mean accuracy.\nMoreover, by using a direct likelihood approximation rather than a variational\none, model confidence estimates are significantly improved.","url_abs":"http://arxiv.org/abs/1705.10813v3","url_pdf":"http://arxiv.org/pdf/1705.10813v3.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":"large-linear-multi-output-gaussian-process","repo_url":"https://github.com/vlad17/runlmc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}