{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:58:34Z","timestamp":1764784714855},"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":[[2021,8]]},"abstract":"<jats:p>In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains where SOPPs naturally occur include natural language processing, speech recognition, and computer vision. Typical SOPPs have an extremely large label set, which grows exponentially as a function of the size of the output. Existing generalization analysis implies generalization bounds with at least a square-root dependency on the cardinality d of the label set, which can be vacuous in practice. In this paper, we significantly improve the state of the art by developing novel high-probability bounds with a logarithmic dependency on d. Furthermore, we leverage the lens of algorithmic stability to develop generalization bounds in expectation without any dependency on d. Our results therefore build a solid theoretical foundation for learning in large-scale SOPPs. Furthermore, we extend our results to learning with weakly dependent data.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/391","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2841-2847","source":"Crossref","is-referenced-by-count":2,"title":["Fine-grained Generalization Analysis of Structured Output Prediction"],"prefix":"10.24963","author":[{"given":"Waleed","family":"Mustafa","sequence":"first","affiliation":[{"name":"TU Kaiserslautern"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunwen","family":"Lei","sequence":"additional","affiliation":[{"name":"University of Birmingham"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine","family":"Ledent","sequence":"additional","affiliation":[{"name":"TU Kaiserslautern"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"Kloft","sequence":"additional","affiliation":[{"name":"TU Kaiserslautern"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:03:01Z","timestamp":1628679781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/391"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/391","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}