{"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/demystifying-black-box-models-with-symbolic","title":"Demystifying Black-box Models with Symbolic Metamodels","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Ahmed M. Alaa","Mihaela van der Schaar"],"abstract":"Understanding the predictions of a machine learning model can be as crucial as the model's accuracy in many application domains. However, the black-box nature of most highly-accurate (complex) models is a major hindrance to their interpretability. To address this issue, we introduce the symbolic metamodeling framework — a general methodology for interpreting predictions by converting \"black-box\" models into \"white-box\" functions that are understandable to human subjects. A symbolic metamodel is a model of a model, i.e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation. We parameterize symbolic metamodels using Meijer G-functions — a class of complex-valued contour integrals that depend on scalar parameters, and whose solutions reduce to familiar elementary, algebraic, analytic and closed-form functions for different parameter settings. This parameterization enables efficient optimization of metamodels via gradient descent, and allows discovering the functional forms learned by a machine learning model with minimal a priori assumptions. We show that symbolic metamodeling provides an all-encompassing framework for model interpretation — all common forms of global and local explanations of a model can be analytically derived from its symbolic metamodel.","url_abs":"http://papers.nips.cc/paper/9308-demystifying-black-box-models-with-symbolic-metamodels","url_pdf":"http://papers.nips.cc/paper/9308-demystifying-black-box-models-with-symbolic-metamodels.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":"demystifying-black-box-models-with-symbolic","repo_url":"https://github.com/ahmedmalaa/Symbolic-Metamodeling","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}