{"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/variational-learning-on-aggregate-outputs","title":"Variational Learning on Aggregate Outputs with Gaussian Processes","arxiv_id":"1805.08463","date":"2018-05-22","proceeding":"NeurIPS 2018 12","authors":["Ho Chung Leon Law","Dino Sejdinovic","Ewan Cameron","Tim CD Lucas","Seth Flaxman","Katherine Battle","Kenji Fukumizu"],"abstract":"While a typical supervised learning framework assumes that the inputs and the\noutputs are measured at the same levels of granularity, many applications,\nincluding global mapping of disease, only have access to outputs at a much\ncoarser level than that of the inputs. Aggregation of outputs makes\ngeneralization to new inputs much more difficult. We consider an approach to\nthis problem based on variational learning with a model of output aggregation\nand Gaussian processes, where aggregation leads to intractability of the\nstandard evidence lower bounds. We propose new bounds and tractable\napproximations, leading to improved prediction accuracy and scalability to\nlarge datasets, while explicitly taking uncertainty into account. We develop a\nframework which extends to several types of likelihoods, including the Poisson\nmodel for aggregated count data. We apply our framework to a challenging and\nimportant problem, the fine-scale spatial modelling of malaria incidence, with\nover 1 million observations.","url_abs":"http://arxiv.org/abs/1805.08463v1","url_pdf":"http://arxiv.org/pdf/1805.08463v1.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":"variational-learning-on-aggregate-outputs","repo_url":"https://github.com/hcllaw/VBAgg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1805.08463","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}