{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T21:23:37Z","timestamp":1747776217772},"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>The compositional minimax problem covers plenty of machine learning models such as the distributionally robust compositional optimization problem. However,  it is yet another understudied problem to optimize the compositional minimax problem. In this paper, we develop a novel efficient stochastic compositional gradient descent ascent method for optimizing the compositional minimax problem. Moreover, we establish the theoretical convergence rate of our proposed method. To the best of our knowledge, this is the first work achieving such a convergence rate for the compositional minimax problem. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed method.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/329","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2389-2395","source":"Crossref","is-referenced-by-count":1,"title":["On the Convergence of Stochastic Compositional Gradient Descent Ascent Method"],"prefix":"10.24963","author":[{"given":"Hongchang","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, PA, USA"}]},{"given":"Xiaoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, IN, USA"}]},{"given":"Lei","family":"Luo","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation, Mountain View, CA, USA"}]},{"given":"Xinghua","family":"Shi","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":"2021-08-11T11:02:39Z","timestamp":1628679759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/329"}},"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\/329","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}