{"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/selecting-representative-examples-for-program","title":"Selecting Representative Examples for Program Synthesis","arxiv_id":"1711.03243","date":"2017-11-09","proceeding":"ICML 2018 7","authors":["Yewen Pu","Zachery Miranda","Armando Solar-Lezama","Leslie Pack Kaelbling"],"abstract":"Program synthesis is a class of regression problems where one seeks a\nsolution, in the form of a source-code program, mapping the inputs to their\ncorresponding outputs exactly. Due to its precise and combinatorial nature,\nprogram synthesis is commonly formulated as a constraint satisfaction problem,\nwhere input-output examples are encoded as constraints and solved with a\nconstraint solver. A key challenge of this formulation is scalability: while\nconstraint solvers work well with a few well-chosen examples, a large set of\nexamples can incur significant overhead in both time and memory. We describe a\nmethod to discover a subset of examples that is both small and representative:\nthe subset is constructed iteratively, using a neural network to predict the\nprobability of unchosen examples conditioned on the chosen examples in the\nsubset, and greedily adding the least probable example. We empirically evaluate\nthe representativeness of the subsets constructed by our method, and\ndemonstrate such subsets can significantly improve synthesis time and\nstability.","url_abs":"http://arxiv.org/abs/1711.03243v3","url_pdf":"http://arxiv.org/pdf/1711.03243v3.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":"selecting-representative-examples-for-program","repo_url":"https://github.com/evanthebouncy/icml2018_selecting_representative_examples","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"program-synthesis","task_name":"Program Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}