{"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/near-optimal-active-learning-of-multi-output","title":"Near-Optimal Active Learning of Multi-Output Gaussian Processes","arxiv_id":"1511.06891","date":"2015-11-21","proceeding":null,"authors":["Yehong Zhang","Trong Nghia Hoang","Kian Hsiang Low","Mohan Kankanhalli"],"abstract":"This paper addresses the problem of active learning of a multi-output\nGaussian process (MOGP) model representing multiple types of coexisting\ncorrelated environmental phenomena. In contrast to existing works, our active\nlearning problem involves selecting not just the most informative sampling\nlocations to be observed but also the types of measurements at each selected\nlocation for minimizing the predictive uncertainty (i.e., posterior joint\nentropy) of a target phenomenon of interest given a sampling budget.\nUnfortunately, such an entropy criterion scales poorly in the numbers of\ncandidate sampling locations and selected observations when optimized. To\nresolve this issue, we first exploit a structure common to sparse MOGP models\nfor deriving a novel active learning criterion. Then, we exploit a relaxed form\nof submodularity property of our new criterion for devising a polynomial-time\napproximation algorithm that guarantees a constant-factor approximation of that\nachieved by the optimal set of selected observations. Empirical evaluation on\nreal-world datasets shows that our proposed approach outperforms existing\nalgorithms for active learning of MOGP and single-output GP models.","url_abs":"http://arxiv.org/abs/1511.06891v2","url_pdf":"http://arxiv.org/pdf/1511.06891v2.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":"near-optimal-active-learning-of-multi-output","repo_url":"https://github.com/sumitsk/MOGP-AL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.06891","atlas_url":"https://app.syntology.ai/?focus=1511.06891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06891"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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