{"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/multi-way-interacting-regression-via","title":"Multi-way Interacting Regression via Factorization Machines","arxiv_id":"1709.09301","date":"2017-09-27","proceeding":"NeurIPS 2017 12","authors":["Mikhail Yurochkin","XuanLong Nguyen","Nikolaos Vasiloglou"],"abstract":"We propose a Bayesian regression method that accounts for multi-way\ninteractions of arbitrary orders among the predictor variables. Our model makes\nuse of a factorization mechanism for representing the regression coefficients\nof interactions among the predictors, while the interaction selection is guided\nby a prior distribution on random hypergraphs, a construction which generalizes\nthe Finite Feature Model. We present a posterior inference algorithm based on\nGibbs sampling, and establish posterior consistency of our regression model.\nOur method is evaluated with extensive experiments on simulated data and\ndemonstrated to be able to identify meaningful interactions in applications in\ngenetics and retail demand forecasting.","url_abs":"http://arxiv.org/abs/1709.09301v1","url_pdf":"http://arxiv.org/pdf/1709.09301v1.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":"multi-way-interacting-regression-via","repo_url":"https://github.com/moonfolk/MiFM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"demand-forecasting","task_name":"Demand Forecasting"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}