{"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/structured-learning-modulo-theories","title":"Structured Learning Modulo Theories","arxiv_id":"1405.1675","date":"2014-05-07","proceeding":null,"authors":["Stefano Teso","Roberto Sebastiani","Andrea Passerini"],"abstract":"Modelling problems containing a mixture of Boolean and numerical variables is\na long-standing interest of Artificial Intelligence. However, performing\ninference and learning in hybrid domains is a particularly daunting task. The\nability to model this kind of domains is crucial in \"learning to design\" tasks,\nthat is, learning applications where the goal is to learn from examples how to\nperform automatic {\\em de novo} design of novel objects. In this paper we\npresent Structured Learning Modulo Theories, a max-margin approach for learning\nin hybrid domains based on Satisfiability Modulo Theories, which allows to\ncombine Boolean reasoning and optimization over continuous linear arithmetical\nconstraints. The main idea is to leverage a state-of-the-art generalized\nSatisfiability Modulo Theory solver for implementing the inference and\nseparation oracles of Structured Output SVMs. We validate our method on\nartificial and real world scenarios.","url_abs":"http://arxiv.org/abs/1405.1675v2","url_pdf":"http://arxiv.org/pdf/1405.1675v2.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":"structured-learning-modulo-theories","repo_url":"https://bitbucket.org/stefanoteso/pylmt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}