{"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/cadre-modeling-simultaneously-discovering","title":"Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models","arxiv_id":"1802.02500","date":"2018-02-07","proceeding":null,"authors":["Alexander New","Curt Breneman","Kristin P. Bennett"],"abstract":"We consider the problem in regression analysis of identifying subpopulations\nthat exhibit different patterns of response, where each subpopulation requires\na different underlying model. Unlike statistical cohorts, these subpopulations\nare not known a priori; thus, we refer to them as cadres. When the cadres and\ntheir associated models are interpretable, modeling leads to insights about the\nsubpopulations and their associations with the regression target. We introduce\na discriminative model that simultaneously learns cadre assignment and\ntarget-prediction rules. Sparsity-inducing priors are placed on the model\nparameters, under which independent feature selection is performed for both the\ncadre assignment and target-prediction processes. We learn models using\nadaptive step size stochastic gradient descent, and we assess cadre quality\nwith bootstrapped sample analysis. We present simulated results showing that,\nwhen the true clustering rule does not depend on the entire set of features,\nour method significantly outperforms methods that learn subpopulation-discovery\nand target-prediction rules separately. In a materials-by-design case study,\nour model provides state-of-the-art prediction of polymer glass transition\ntemperature. Importantly, the method identifies cadres of polymers that respond\ndifferently to structural perturbations, thus providing design insight for\ntargeting or avoiding specific transition temperature ranges. It identifies\nchemically meaningful cadres, each with interpretable models. Further\nexperimental results show that cadre methods have generalization that is\ncompetitive with linear and nonlinear regression models and can identify robust\nsubpopulations.","url_abs":"http://arxiv.org/abs/1802.02500v2","url_pdf":"http://arxiv.org/pdf/1802.02500v2.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":"cadre-modeling-simultaneously-discovering","repo_url":"https://github.com/newalexander/supervised-cadres","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"feature-selection","task_name":"feature selection"},{"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}