{"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/manifold-gaussian-processes-for-regression","title":"Manifold Gaussian Processes for Regression","arxiv_id":"1402.5876","date":"2014-02-24","proceeding":null,"authors":["Roberto Calandra","Jan Peters","Carl Edward Rasmussen","Marc Peter Deisenroth"],"abstract":"Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness\nassumptions on the structure of the function to be modeled. To model complex\nand non-differentiable functions, these smoothness assumptions are often too\nrestrictive. One way to alleviate this limitation is to find a different\nrepresentation of the data by introducing a feature space. This feature space\nis often learned in an unsupervised way, which might lead to data\nrepresentations that are not useful for the overall regression task. In this\npaper, we propose Manifold Gaussian Processes, a novel supervised method that\njointly learns a transformation of the data into a feature space and a GP\nregression from the feature space to observed space. The Manifold GP is a full\nGP and allows to learn data representations, which are useful for the overall\nregression task. As a proof-of-concept, we evaluate our approach on complex\nnon-smooth functions where standard GPs perform poorly, such as step functions\nand robotics tasks with contacts.","url_abs":"http://arxiv.org/abs/1402.5876v4","url_pdf":"http://arxiv.org/pdf/1402.5876v4.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":"manifold-gaussian-processes-for-regression","repo_url":"https://github.com/nehap25/rlwithgp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"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=1402.5876","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}