{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:00:50Z","timestamp":1782968450849,"version":"3.54.5"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A5A7083908"],"award-info":[{"award-number":["2022R1A5A7083908"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A2C3A01003550"],"award-info":[{"award-number":["2020R1A2C3A01003550"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tpami.2025.3538915","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T18:51:30Z","timestamp":1738781490000},"page":"3784-3795","source":"Crossref","is-referenced-by-count":2,"title":["Fair Representation Learning for Continuous Sensitive Attributes Using Expectation of Integral Probability Metrics"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3508-6672","authenticated-orcid":false,"given":"Insung","family":"Kong","sequence":"first","affiliation":[{"name":"Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2750-2463","authenticated-orcid":false,"given":"Kunwoong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Statistics, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9434-5645","authenticated-orcid":false,"given":"Yongdai","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Statistics, Seoul National University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"ref3","first-page":"962","article-title":"Fairness constraints: Mechanisms for fair classification","volume-title":"Proc. Artif. Intell. Statist.","author":"Zafar"},{"key":"ref4","first-page":"2791","article-title":"Empirical risk minimization under fairness constraints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Donini"},{"key":"ref5","first-page":"60","article-title":"A reductions approach to fair classification","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Agarwal"},{"key":"ref6","first-page":"325","article-title":"Learning fair representations","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Zemel"},{"key":"ref7","article-title":"The variational fair autoencoder","author":"Louizos","year":"2015"},{"key":"ref8","first-page":"1","article-title":"Censoring representations with an adversary","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Edwards"},{"key":"ref9","first-page":"3995","article-title":"Optimized pre-processing for discrimination prevention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Calmon"},{"key":"ref10","first-page":"3384","article-title":"Learning adversarially fair and transferable representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Madras"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00842"},{"key":"ref12","article-title":"Learning fair representations via an adversarial framework","author":"Feng","year":"2019"},{"key":"ref13","first-page":"2164","article-title":"Learning controllable fair representations","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","author":"Song"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_44"},{"key":"ref15","first-page":"7584","article-title":"Learning certified individually fair representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ruoss"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16931"},{"key":"ref17","first-page":"253","article-title":"Learning smooth and fair representations","volume-title":"Proc. 24th Int. Conf. Artif. Intell. Statist.","author":"Gitiaux"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18111"},{"key":"ref19","first-page":"11074","article-title":"Learning fair representation with a parametric integral probability metric","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref20","first-page":"20156","article-title":"Fair representation learning through implicit path alignment","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shui"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539232"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3187165"},{"key":"ref23","first-page":"15401","article-title":"FARE: Provably fair representation learning with practical certificates","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Jovanovi\u0107"},{"key":"ref24","first-page":"9564","article-title":"MMD-B-fair: Learning fair representations with statistical testing","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Deka"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3148905"},{"key":"ref26","first-page":"4382","article-title":"Fairness-aware learning for continuous attributes and treatments","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mary"},{"key":"ref27","first-page":"2262","article-title":"Fairness-aware neural r\u00e9nyi minimization for continuous features","volume-title":"Proc. 29th Int. Conf. Int. Joint Conf. Artif. Intell.","author":"Grari"},{"key":"ref28","article-title":"Generalized demographic parity for group fairness","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jiang"},{"key":"ref29","first-page":"11443","article-title":"Generalized disparate impact for configurable fairness solutions in ML","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Giuliani"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2477899"},{"key":"ref32","article-title":"Fair mixup: Fairness via interpolation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chuang","year":"2021"},{"key":"ref33","first-page":"15088","article-title":"A fair classifier using kernel density estimation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cho"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1137\/1109020"},{"key":"ref35","first-page":"359","article-title":"Smooth regression analysis","volume":"26","author":"Watson","year":"1964","journal-title":"Sankhy\u0101 Indian J. Statist., Ser. A"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00240"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref39","first-page":"1436","article-title":"Flexibly fair representation learning by disentanglement","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Creager"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00842"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"ref42","first-page":"1673","article-title":"Optimized score transformation for fair classification","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Statist.","author":"Wei"},{"key":"ref43","first-page":"862","article-title":"Wasserstein fair classification","volume-title":"Proc. 35th Uncertainty Artif. Intell. Conf.","author":"Jiang"},{"key":"ref44","first-page":"2415","article-title":"Satisfying real-world goals with dataset constraints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Goh"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61527-7_38"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20699"},{"key":"ref47","first-page":"52","article-title":"On a space of totally additive functions","volume":"13","author":"Kantorovich","year":"1958","journal-title":"Vestnik, Leningrad Univ."},{"key":"ref48","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref49","first-page":"11816","article-title":"Distributional robustness with IPMs and links to regularization and GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Husain"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.2307\/1428011"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1214\/20-AOS2034"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9574.00226"},{"issue":"1","key":"ref53","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref54","first-page":"2200","article-title":"MMD GAN: Towards deeper understanding of moment matching network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref55","first-page":"17430","article-title":"Covariate balancing using the integral probability metric for causal inference","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Kong"},{"key":"ref56","first-page":"67","article-title":"On the influence of the kernel on the consistency of support vector machines","volume":"2","author":"Steinwart","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref57","volume-title":"Support Vector Machines","author":"Steinwart","year":"2008"},{"key":"ref58","first-page":"3315","article-title":"Equality of opportunity in supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hardt"},{"key":"ref59","article-title":"Conditional probability density and regression estimators","volume":"25","author":"Rosenblatt","year":"1969","journal-title":"Multivariate Anal. II"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1214\/12-ejs722"},{"key":"ref61","article-title":"The curse of dimensionality for local kernel machines","author":"Bengio","year":"2005"},{"key":"ref62","volume-title":"Information Theory, Inference, and Learning Algorithms","author":"MacKay","year":"2003"},{"key":"ref63","first-page":"972","article-title":"Self-normalizing neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Klambauer"},{"key":"ref64","first-page":"10","article-title":"Learning from distributions via support measure machines","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Muandet"},{"key":"ref65","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref66","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0087357"},{"issue":"2","key":"ref68","first-page":"9","article-title":"Sample estimate of the entropy of a random vector","volume":"23","author":"Kozachenko","year":"1987","journal-title":"Problemy Peredachi Informatsii"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00417"},{"key":"ref70","article-title":"Learn what matters: Cross-domain imitation learning with task-relevant embeddings","author":"Franzmeyer","year":"2022"},{"key":"ref71","first-page":"18707","article-title":"Robust evaluation measures for evaluating social biases in masked language models","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Liu"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10958761\/10874180.pdf?arnumber=10874180","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T18:19:17Z","timestamp":1744654757000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10874180\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":71,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3538915","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}