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In addition, we introduce two oversampling methods for PdFairDisCo to mitigate bias by balancing the proportion of the sensitive attribute in training data. We demonstrate the effectiveness of PdFairDisCo through experiments. The experiments also show that the oversampling methods can further improve the performance of fairness.<\/jats:p>","DOI":"10.1007\/s00354-026-00320-0","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T08:13:46Z","timestamp":1774426426000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Fair Representation Learning with Variational Autoencoders"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8674-4829","authenticated-orcid":false,"given":"Tatsuya","family":"Yamada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9131-4664","authenticated-orcid":false,"given":"Takuya","family":"Konishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7789-4709","authenticated-orcid":false,"given":"Yoshinobu","family":"Kawahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"issue":"6","key":"320_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2022","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. 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