{"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-inference-in-multi-task-cox-process","title":"Efficient Inference in Multi-task Cox Process Models","arxiv_id":"1805.09781","date":"2018-05-24","proceeding":null,"authors":["Virginia Aglietti","Theodoros Damoulas","Edwin Bonilla"],"abstract":"We generalize the log Gaussian Cox process (LGCP) framework to model multiple\ncorrelated point data jointly. The observations are treated as realizations of\nmultiple LGCPs, whose log intensities are given by linear combinations of\nlatent functions drawn from Gaussian process priors. The combination\ncoefficients are also drawn from Gaussian processes and can incorporate\nadditional dependencies. We derive closed-form expressions for the moments of\nthe intensity functions and develop an efficient variational inference\nalgorithm that is orders of magnitude faster than competing deterministic and\nstochastic approximations of multivariate LGCP, coregionalization models, and\nmulti-task permanental processes. Our approach outperforms these benchmarks in\nmultiple problems, offering the current state of the art in modeling\nmultivariate point processes.","url_abs":"http://arxiv.org/abs/1805.09781v3","url_pdf":"http://arxiv.org/pdf/1805.09781v3.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-inference-in-multi-task-cox-process","repo_url":"https://github.com/VirgiAgl/MCPM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"point-processes","task_name":"Point Processes"},{"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=1805.09781","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}