{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:47Z","timestamp":1772908667338,"version":"3.50.1"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100018694","name":"Marie Sklodowska-Curie Actions","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825191","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"1605-1614","source":"Crossref","is-referenced-by-count":1,"title":["Adversarial Reweighting Guided by Wasserstein Distance to Achieve Demographic Parity"],"prefix":"10.1109","author":[{"given":"Xuan","family":"Zhao","sequence":"first","affiliation":[{"name":"SCHUFA Holding AG,Wiesabden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Fabbrizzi","sequence":"additional","affiliation":[{"name":"CERTH-ITI,Thessaloniki,Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paula Reyero","family":"Lobo","sequence":"additional","affiliation":[{"name":"The Open University,Milton Keynes,UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siamak","family":"Ghodsi","sequence":"additional","affiliation":[{"name":"Leibniz University of Hannover,Hannover,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus","family":"Broelemann","sequence":"additional","affiliation":[{"name":"SCHUFA Holding AG,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Staab","sequence":"additional","affiliation":[{"name":"Universität Stuttgart,Stuttgart,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gjergji","family":"Kasneci","sequence":"additional","affiliation":[{"name":"Techinical Universitzy of Munich,Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011418"},{"key":"ref2","article-title":"A convex framework for fair regression","author":"Berk","year":"2017","journal-title":"CoRR"},{"key":"ref3","first-page":"1436","article-title":"Flexibly fair representation learning by disentanglement","volume":"97","author":"Creager","year":"2019","journal-title":"ICML"},{"key":"ref4","first-page":"702","article-title":"Identifying and correcting label bias in machine learning","volume":"108","author":"Jiang","year":"2020","journal-title":"AISTATS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186133"},{"key":"ref6","first-page":"5767","article-title":"Improved training of wasserstein gans","author":"Gulrajani","year":"2017","journal-title":"NIPS"},{"key":"ref7","first-page":"2853","article-title":"Fairness with adaptive weights","volume":"162","author":"Chai","year":"2022","journal-title":"ICML"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012412"},{"key":"ref9","first-page":"3381","article-title":"Learning adversarially fair and transferable representations","volume":"80","author":"Madras","year":"2018","journal-title":"ICML"},{"key":"ref10","article-title":"Wasserstein gans work because they fail (to approximate the wasserstein distance)","author":"Stanczuk","year":"2021","journal-title":"CoRR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"ref12","first-page":"862","article-title":"Wasserstein fair classification","volume":"115","author":"Jiang","year":"2019","journal-title":"UAI"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00658-8"},{"key":"ref14","article-title":"Fairness with continuous optimal transport","author":"Chiappa","year":"2021","journal-title":"CoRR"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012412"},{"key":"ref16","first-page":"851","article-title":"Why can\u2019t I dance in the mall? learning to mitigate scene bias in action recognition","author":"Choi","year":"2019","journal-title":"NeurIPS"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00922"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411980"},{"key":"ref19","article-title":"Fairness without demographics through adversarially reweighted learning","author":"Lahoti","year":"2020","journal-title":"NeurIPS"},{"key":"ref20","first-page":"14860","article-title":"Adversarial reweighting for partial domain adaptation","author":"Gu","year":"2021","journal-title":"NeurIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1089\/big.2016.0047"},{"key":"ref22","first-page":"8024","article-title":"Pytorch: An imperative style, highperformance deep learning library","author":"Paszke","year":"2019","journal-title":"NeurIPS"},{"issue":"83","key":"ref23","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","author":"Diamond","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref27","first-page":"71","article-title":"Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes","volume-title":"ICML (1), ser. JMLR Workshop and Conference Proceedings","volume":"28","author":"Shamir"},{"key":"ref28","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015","journal-title":"ICLR (Poster)"},{"key":"ref29","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","author":"Kohavi","year":"1996","journal-title":"KDD"},{"key":"ref30","article-title":"UCI machine learning repository","author":"Dua","year":"2017"},{"key":"ref31","article-title":"Deep neural networks and tabular data: A survey","author":"Borisov","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref32","article-title":"How well do wgans estimate the wasserstein metric?","author":"Mallasto","year":"2019","journal-title":"CoRR"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","location":"Washington, DC, USA","start":{"date-parts":[[2024,12,15]]},"end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825191.pdf?arnumber=10825191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:45:56Z","timestamp":1737099956000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825191\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825191","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}