{"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/heterogeneous-multi-output-gaussian-process","title":"Heterogeneous Multi-output Gaussian Process Prediction","arxiv_id":"1805.07633","date":"2018-05-19","proceeding":"NeurIPS 2018 12","authors":["Pablo Moreno-Muñoz","Antonio Artés-Rodríguez","Mauricio A. Álvarez"],"abstract":"We present a novel extension of multi-output Gaussian processes for handling\nheterogeneous outputs. We assume that each output has its own likelihood\nfunction and use a vector-valued Gaussian process prior to jointly model the\nparameters in all likelihoods as latent functions. Our multi-output Gaussian\nprocess uses a covariance function with a linear model of coregionalisation\nform. Assuming conditional independence across the underlying latent functions\ntogether with an inducing variable framework, we are able to obtain tractable\nvariational bounds amenable to stochastic variational inference. We illustrate\nthe performance of the model on synthetic data and two real datasets: a human\nbehavioral study and a demographic high-dimensional dataset.","url_abs":"http://arxiv.org/abs/1805.07633v2","url_pdf":"http://arxiv.org/pdf/1805.07633v2.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":"heterogeneous-multi-output-gaussian-process","repo_url":"https://github.com/pmorenoz/HetMOGP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"prediction","task_name":"Prediction"},{"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=1805.07633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07633"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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