{"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/efficient-modeling-of-latent-information-in","title":"Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes","arxiv_id":"1705.09862","date":"2017-05-27","proceeding":"NeurIPS 2017 12","authors":["Zhenwen Dai","Mauricio A. Álvarez","Neil D. Lawrence"],"abstract":"Often in machine learning, data are collected as a combination of multiple\nconditions, e.g., the voice recordings of multiple persons, each labeled with\nan ID. How could we build a model that captures the latent information related\nto these conditions and generalize to a new one with few data? We present a new\nmodel called Latent Variable Multiple Output Gaussian Processes (LVMOGP) and\nthat allows to jointly model multiple conditions for regression and generalize\nto a new condition with a few data points at test time. LVMOGP infers the\nposteriors of Gaussian processes together with a latent space representing the\ninformation about different conditions. We derive an efficient variational\ninference method for LVMOGP, of which the computational complexity is as low as\nsparse Gaussian processes. We show that LVMOGP significantly outperforms\nrelated Gaussian process methods on various tasks with both synthetic and real\ndata.","url_abs":"http://arxiv.org/abs/1705.09862v1","url_pdf":"http://arxiv.org/pdf/1705.09862v1.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":"efficient-modeling-of-latent-information-in","repo_url":"https://github.com/rsedgwick/tl_doe_4_dna","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"efficient-modeling-of-latent-information-in","repo_url":"https://github.com/rsedgwick/tlgps","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.09862","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}