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Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Generative adversarial networks have become a\n            <jats:italic>de facto<\/jats:italic>\n            approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic data generation. Additional constraints such as privacy preservation, distribution realism, and diversity promotion may also be essential to optimize. To address this challenge, we introduce\n            <jats:italic>HydraGAN<\/jats:italic>\n            , a multi-agent network that performs multi-objective synthetic data generation. We theoretically verify that training the HydraGAN system, containing a single generator and an arbitrary number of discriminators, leads to a Nash equilibrium. Experimental results for six datasets indicate that HydraGAN consistently outperforms prior methods in maximizing the Area under the Radar Chart, balancing a combination of cooperative or competitive data generation goals.\n          <\/jats:p>","DOI":"10.1145\/3653982","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T12:00:29Z","timestamp":1712318429000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8670-7378","authenticated-orcid":false,"given":"Chance","family":"DeSmet","sequence":"first","affiliation":[{"name":"Washington State University, Pullman, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4441-7508","authenticated-orcid":false,"given":"Diane","family":"Cook","sequence":"additional","affiliation":[{"name":"Washington State University College of Engineering and Architecture, Pullman, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-70992-5_2"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2017.7917555"},{"key":"e_1_3_3_4_2","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. 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