{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T09:23:49Z","timestamp":1760606629104,"version":"3.37.3"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean Government Ministry of Science and ICT","doi-asserted-by":"publisher","award":["NRF-2019R1A2C2002358","2017R1A5A1015626"],"award-info":[{"award-number":["NRF-2019R1A2C2002358","2017R1A5A1015626"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3005987","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T20:49:59Z","timestamp":1593550199000},"page":"123783-123798","source":"Crossref","is-referenced-by-count":13,"title":["Joint Transfer of Model Knowledge and Fairness Over Domains Using Wasserstein Distance"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0508-0081","authenticated-orcid":false,"given":"Taeho","family":"Yoon","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5720-8337","authenticated-orcid":false,"given":"Jaewook","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7432-4185","authenticated-orcid":false,"given":"Woojin","family":"Lee","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref32","first-page":"435","article-title":"Wasserstein barycenter and its application to texture mixing","author":"rabin","year":"2011","journal-title":"Int Conf Scale Space Variational Methods Comput Vis"},{"key":"ref31","article-title":"Towards principled methods for training generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 04862"},{"key":"ref30","first-page":"1","article-title":"Wasserstein distance guided representation learning for domain adaptation","author":"shen","year":"2018","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref35","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11217"},{"key":"ref10","first-page":"325","article-title":"Learning fair representations","author":"zemel","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","first-page":"3384","article-title":"Learning adversarially fair and transferable representations","author":"madras","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","article-title":"Conditional learning of fair representations","author":"zhao","year":"2019","journal-title":"arXiv 1910 07162"},{"key":"ref13","first-page":"985","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"sugiyama","year":"2007","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.7232\/iems.2018.17.2.334"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref16","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2015","journal-title":"J Mach Learn Res"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-184131"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2865249"},{"key":"ref28","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref4","first-page":"3315","article-title":"Equality of opportunity in supervised learning","author":"hardt","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","volume":"338","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"ref6","first-page":"2791","article-title":"Empirical risk minimization under fairness constraints","author":"donini","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref5","first-page":"1","article-title":"Fairness in machine learning","author":"barocas","year":"2017","journal-title":"NIPS Tutorial"},{"key":"ref8","article-title":"Using Wasserstein-2 regularization to ensure fair decisions with neural-network classifiers","author":"risser","year":"2019","journal-title":"arXiv 1908 05783"},{"key":"ref7","article-title":"Wasserstein fair classification","author":"jiang","year":"2019","journal-title":"arXiv 1907 12059"},{"key":"ref2","first-page":"2357","article-title":"Obtaining fairness using optimal transport theory","author":"gordaliza","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"},{"key":"ref1","first-page":"1","article-title":"Training individually fair ML models with sensitive subspace robustness","author":"yurochkin","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2944226"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916935"},{"key":"ref21","first-page":"137","article-title":"Analysis of representations for domain adaptation","author":"ben-david","year":"2007","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","article-title":"Transfer of machine learning fairness across domains","author":"schumann","year":"2019","journal-title":"arXiv 1906 09688"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314236"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10958-006-0049-2"},{"key":"ref25","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09129731.pdf?arnumber=9129731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T01:09:48Z","timestamp":1641949788000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9129731\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3005987","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}