{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T16:46:22Z","timestamp":1743266782216},"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>Continuous DR-submodular maximization is an important machine learning problem, which covers numerous popular applications.  With the emergence of large-scale distributed data, developing efficient algorithms for the continuous DR-submodular maximization, such as the decentralized Frank-Wolfe method, became an important challenge. However, existing decentralized Frank-Wolfe methods for this kind of problem have the sample complexity of $\\mathcal{O}(1\/\\epsilon^3)$, incurring a large computational overhead. In this paper, we propose two novel sample efficient decentralized Frank-Wolfe methods to address this challenge. Our theoretical results demonstrate that the sample complexity of the two proposed methods is $\\mathcal{O}(1\/\\epsilon^2)$, which is better than $\\mathcal{O}(1\/\\epsilon^3)$ of the existing methods. As far as we know, this is the first published result achieving such a favorable sample complexity. Extensive experimental results confirm the effectiveness of the proposed methods.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/482","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"3501-3507","source":"Crossref","is-referenced-by-count":5,"title":["Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization"],"prefix":"10.24963","author":[{"given":"Hongchang","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, PA, USA"}]},{"given":"Hanzi","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, PA, USA"}]},{"given":"Slobodan","family":"Vucetic","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, PA, USA"}]}],"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":"2021-08-11T11:03:37Z","timestamp":1628679817000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/482"}},"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\/482","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}