At the dated check, the references listed below either did not resolve in
Crossref or DataCite, or carried a retraction notice. Each one is shown with the
registry record that put it there.
The 40 references without a DOI — listed, not checked
no DOI — not checkedTameem Adel, Zoubin Ghahramani, and Adrian Weller. 2018. Discovering interpretable representations for both deep generative and discriminative models. In Proceedings of the International Conference on Machine Learning. PMLR, 50–59.
no DOI — not checkedAlekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu. 2019. Fair regression: Quantitative definitions and reduction-based algorithms. In Proceedings of the International Conference on Machine Learning. PMLR, 120–129.
no DOI — not checkedJulia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23, 2016 (2016), 139–159.
no DOI — not checkedArturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner. 2019. Scalable fair clustering. In Proceedings of the International Conference on Machine Learning. PMLR, 405–413.
no DOI — not checkedRichard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A convex framework for fair regression. arXiv:1706.02409. Retrieved from https://arxiv.org/abs/1706.02409.
no DOI — not checkedAvishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In Proceedings of the International Conference on Machine Learning. PMLR, 715–724.
no DOI — not checkedSabri Boughorbel Fethi Jarray and Abdou Kadri. 2021. Fairness in TabNet model by disentangled representation for the prediction of hospital no-show. arXiv:2103.04048. Retrieved from https://arxiv.org/abs/2103.04048.
no DOI — not checkedSimon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv:2010.04053. Retrieved from https://arxiv.org/abs/2010.04053.
no DOI — not checkedPengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, and Lawrence Carin. 2021. FairFil: Contrastive neural debiasing method for pretrained text encoders. In Proceedings of the International Conference on Learning Representations.
no DOI — not checkedFlavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii. 2017. Fair clustering through fairlets. Advances in Neural Information Processing Systems 30 (2017).
no DOI — not checkedJacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
no DOI — not checkedPietro G. Di Stefano James M. Hickey and Vlasios Vasileiou. 2020. Counterfactual fairness: Removing direct effects through regularization. arXiv:2002.10774. Retrieved from https://arxiv.org/abs/2002.10774.
no DOI — not checkedMengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Awadallah, and Xia Hu. 2021. Fairness via representation neutralization. Advances in Neural Information Processing Systems 34 (2021).
no DOI — not checkedMengnan Du, Fan Yang, Na Zou, and Xia Hu. 2020. Fairness in deep learning: A computational perspective. IEEE Intelligent Systems (2020).
no DOI — not checkedHarrison Edwards and Amos Storkey. 2015. Censoring representations with an adversary. arXiv:1511.05897. Retrieved from https://arxiv.org/abs/1511.05897.
no DOI — not checkedMaya Gupta Andrew Cotter Mahdi Milani Fard and Serena Wang. 2018. Proxy fairness. arXiv:1806.11212. Retrieved from https://arxiv.org/abs/1806.11212.
no DOI — not checkedMoritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems 29 (2016).
no DOI — not checkedTatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without demographics in repeated loss minimization. In Proceedings of the International Conference on Machine Learning. PMLR, 1929–1938.
no DOI — not checkedRay Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, and Silvia Chiappa. 2020. Wasserstein fair classification. In Uncertainty in Artificial Intelligence. PMLR, 862–872.
no DOI — not checkedZhimeng Jiang Xiaotian Han Chao Fan Zirui Liu Na Zou Ali Mostafavi and Xia Hu. 2022. FMP: Toward fair graph message passing against topology bias. arXiv:2202.04187. Retrieved from https://arxiv.org/abs/2202.04187.
no DOI — not checkedMichael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In Proceedings of the International Conference on Machine Learning. PMLR, 2564–2572.
no DOI — not checkedMichael P. Kim, Omer Reingold, and Guy N. Rothblum. 2018. Fairness through computationally-bounded awareness. In Proceedings of the International Conference on Neural Information Processing Systems.
no DOI — not checkedÖykü Deniz Köse and Yanning Shen. 2021. Fairness-aware node representation learning. arXiv:2106.05391. Retrieved from https://arxiv.org/abs/2106.05391.
no DOI — not checkedMatt J. Kusner Joshua R. Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual fairness. arXiv:1703.06856. Retrieved from https://arxiv.org/abs/1703.06856.
no DOI — not checkedPreethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi. 2020. Fairness without demographics through adversarially reweighted learning. In Proceedings of the International Conference on Neural Information Processing Systems.
no DOI — not checkedFrancesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem. 2019. On the fairness of disentangled representations. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32. 14611–14624.
no DOI — not checkedDavid Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018. Learning adversarially fair and transferable representations. In Proceedings of the International Conference on Machine Learning. PMLR, 3384–3393.
no DOI — not checkedDana Pessach and Erez Shmueli. 2020. Algorithmic fairness. arXiv:2001.09784. Retrieved from https://arxiv.org/abs/2001.09784.
no DOI — not checkedGeoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger. 2017. On fairness and calibration. In Proceedings of the International Conference on Neural Information Processing Systems.
no DOI — not checkedYuji Roh Kangwook Lee Steven Euijong Whang and Changho Suh. 2020. Fairbatch: Batch selection for model fairness. arXiv:2012.01696. Retrieved from https://arxiv.org/abs/2012.01696.
no DOI — not checkedAndrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez. 2007. Right for the right reasons: Training differentiable models by constraining their explanations. In Proceedings of the International Joint Conference on Artificial Intelligence.
no DOI — not checkedCropanzano Russell. 2001. Three roads to organizational justice. (2001).
no DOI — not checkedShiori Sagawa Pang Wei Koh Tatsunori B. Hashimoto and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv:1911.08731. Retrieved from https://arxiv.org/abs/1911.0873.
no DOI — not checkedMelanie Schmidt Chris Schwiegelshohn and Christian Sohler. 2018. Fair coresets and streaming algorithms for fair k-means clustering. arXiv:1812.10854. Retrieved from https://arxiv.org/abs/1812.10854.
no DOI — not checkedYujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. 2020. Interfacegan: Interpreting the disentangled face representation learned by gans. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
no DOI — not checkedYao-Hung Hubert Tsai Martin Q. Ma Han Zhao Kun Zhang Louis-Philippe Morency and Ruslan Salakhutdinov. 2021. Conditional contrastive learning: Removing undesirable information in self-supervised representations. arXiv:2106.02866. Retrieved from https://arxiv.org/abs/2106.02866.
no DOI — not checkedChristina Wadsworth Francesca Vera and Chris Piech. 2018. Achieving fairness through adversarial learning: An application to recidivism prediction. arXiv:1807.00199. Retrieved from https://arxiv.org/abs/1807.00199.
no DOI — not checkedAngelina Wang and Olga Russakovsky. 2021. Directional bias amplification. arXiv:2102.12594. Retrieved from https://arxiv.org/abs/2102.12594.
no DOI — not checkedMichael Wick, Swetasudha Panda, and Jean-Baptiste Tristan. 2019. Unlocking fairness: A trade-off revisited. In Proceedings of the International Conference on Neural Information Processing Systems. 8783–8792.
no DOI — not checkedMuhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. In Proceedings of the Artificial Intelligence and Statistics. PMLR, 962–970.
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