{"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/generalized-robust-bayesian-committee-machine","title":"Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression","arxiv_id":"1806.00720","date":"2018-06-03","proceeding":"ICML 2018 7","authors":["Haitao Liu","Jianfei Cai","Yi Wang","Yew-Soon Ong"],"abstract":"In order to scale standard Gaussian process (GP) regression to large-scale\ndatasets, aggregation models employ factorized training process and then\ncombine predictions from distributed experts. The state-of-the-art aggregation\nmodels, however, either provide inconsistent predictions or require\ntime-consuming aggregation process. We first prove the inconsistency of typical\naggregations using disjoint or random data partition, and then present a\nconsistent yet efficient aggregation model for large-scale GP. The proposed\nmodel inherits the advantages of aggregations, e.g., closed-form inference and\naggregation, parallelization and distributed computing. Furthermore,\ntheoretical and empirical analyses reveal that the new aggregation model\nperforms better due to the consistent predictions that converge to the true\nunderlying function when the training size approaches infinity.","url_abs":"http://arxiv.org/abs/1806.00720v1","url_pdf":"http://arxiv.org/pdf/1806.00720v1.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":"generalized-robust-bayesian-committee-machine","repo_url":"https://github.com/LiuHaiTao01/GRBCM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.00720","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}