{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T02:37:57Z","timestamp":1773542277480,"version":"3.50.1"},"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":[[2018,7]]},"abstract":"<jats:p>Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using domain expertise to manually specify constraints. Learning requires a black-box simulator of structured outputs, which generates valid labels, but need not model their corresponding inputs or the input-label relationship. At training time, we constrain the model to produce outputs that cannot be distinguished from simulated labels by adversarial training. Providing our framework with a small number of labeled inputs gives rise to a new semi-supervised structured prediction model; we evaluate this model on multiple tasks --- tracking, pose estimation and time series prediction --- and find that it achieves high accuracy with only a small number of labeled inputs. In some cases, no labels are required at all.\u00a0<\/jats:p>","DOI":"10.24963\/ijcai.2018\/366","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2637-2643","source":"Crossref","is-referenced-by-count":4,"title":["Adversarial Constraint Learning for Structured Prediction"],"prefix":"10.24963","author":[{"given":"Hongyu","family":"Ren","sequence":"first","affiliation":[{"name":"Department of Computer Science, Stanford University"}]},{"given":"Russell","family":"Stewart","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University"}]},{"given":"Jiaming","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University"}]},{"given":"Volodymyr","family":"Kuleshov","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University"}]},{"given":"Stefano","family":"Ermon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:52:02Z","timestamp":1530769922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/366"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/366","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}