{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:32:46Z","timestamp":1723015966798},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Programming-by-example technologies let end users construct and run\n\nnew programs by providing examples of the intended program behavior.\n\nBut, the few provided examples seldom uniquely determine the intended\n\nprogram.  Previous approaches to picking a program used a bias toward\n\nshorter or more naturally structured programs.  Our work here gives a\n\nmachine learning approach for learning to learn programs that departs\n\nfrom previous work by relying upon features that are independent of\n\nthe program structure, instead relying upon a learned bias over\n\nprogram behaviors, and more generally over program execution\n\ntraces. Our approach leverages abundant unlabeled data for\n\nsemisupervised learning, and incorporates simple kinds of world\n\nknowledge for common-sense reasoning during program induction. These\n\ntechniques are evaluated in two programming-by-example domains,\n\nimproving the accuracy of program learners.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/227","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"1638-1645","source":"Crossref","is-referenced-by-count":10,"title":["Learning to Learn Programs from Examples: Going Beyond Program Structure"],"prefix":"10.24963","author":[{"given":"Kevin","family":"Ellis","sequence":"first","affiliation":[{"name":"MIT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumit","family":"Gulwani","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:52:53Z","timestamp":1501242773000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/227"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/227","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}