{"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-gaussian-process-classification","title":"Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation","arxiv_id":"1802.06383","date":"2018-02-18","proceeding":null,"authors":["Florian Wenzel","Theo Galy-Fajou","Christan Donner","Marius Kloft","Manfred Opper"],"abstract":"We propose a scalable stochastic variational approach to GP classification\nbuilding on Polya-Gamma data augmentation and inducing points. Unlike former\napproaches, we obtain closed-form updates based on natural gradients that lead\nto efficient optimization. We evaluate the algorithm on real-world datasets\ncontaining up to 11 million data points and demonstrate that it is up to two\norders of magnitude faster than the state-of-the-art while being competitive in\nterms of prediction performance.","url_abs":"http://arxiv.org/abs/1802.06383v2","url_pdf":"http://arxiv.org/pdf/1802.06383v2.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-gaussian-process-classification","repo_url":"https://github.com/theogf/AugmentedGaussianProcesses.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"efficient-gaussian-process-classification","repo_url":"https://github.com/UnofficialJuliaMirror/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"efficient-gaussian-process-classification","repo_url":"https://github.com/UnofficialJuliaMirrorSnapshots/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"polya-gamma-augmentation","method_name":"Polya-Gamma Augmentation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1802.06383","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}