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刘文炎 等:可信机器学习的公平性综述 1423
[20] Calders T, Verwer S. Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery,
2010,21(2):277−292.
[21] Edwards H, Storkey AJ. Censoring representations with an adversary. In: Proc. of the ICLR (Poster). 2016.
[22] Louizos C, Swersky K, Li YJ, Welling M, Zemel RS. The variational fair autoencoder. In: Proc. of the ICLR. 2016.
[23] Zemel RS, Wu Y, Swersky K, Pitassi T, Dwork C. Learning fair representations. In: Proc. of the ICML. 2013. 325−333.
[24] Madras D, Creager E, Pitassi T, Zemel RS. Learning adversarially fair and transferable representations. In: Proc. of the ICML. 2018.
3381−3390.
[25] Adel T, Valera I, Ghahramani Z, Weller A. One-network adversarial fairness. In: Proc. of the AAAI. 2019. 2412−2420.
[26] Feldman M, Friedler SA, Moeller J, Scheidegger C, Venkatasubramanian S. Certifying and removing disparate impact. In: Proc. of
the KDD. 2015. 259−268.
[27] Zafar MB, Valera I, Gomez-Rodriguez M, Gummadi KP. Fairness constraints: Mechanisms for fair classification. In: Proc. of the
AISTATS. 2017. 962−970.
[28] Berk R, Heidari H, Jabbari S, Michael K, Roth A. Fairness in criminal justice risk assessments: The state of the art. In: Proc. of the
Sociological Methods and Research. 2018. 3−44.
[29] Kleinberg JM, Mullainathan S, Raghavan M. Inherent trade-offs in the fair determination of risk scores. In: Proc. of the ITCS. 2017.
1−23.
[30] Chouldechova A. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 2017,5(2):
153−163.
[31] Pearl J. Causality. Cambridge University Press, 2009.
[32] Chiappa S. Path-specific counterfactual fairness. In: Proc. of the AAAI. 2019. 7801−7808.
[33] Loftus JR, Russell C, Kusner MJ, Silva R. Causal reasoning for algorithmic fairness. arXiv preprint arXiv:1805.05859v1, 2018.
[34] Beutel A, Chen JL, Zhao Z, Chi EH. Data decisions and theoretical implications when adversarially learning fair representations.
arXiv preprint arXiv:1707.00075v2, 2017.
[35] Zhao H, Gordon GJ. Inherent tradeoffs in learning fair representations. In: Proc. of the NeurIPS. 2019. 15649−15659.
[36] Khosravifard M, Fooladivanda D, Gulliver TA. Confliction of the convexity and metric properties in f-divergences. IEICE Trans.
on Fundamentals of Electronics, Communications and Computer Sciences, 2017,90(9):1848−1853.
[37] Zhao H, Coston A, Adel T, Gordon GJ. Conditional learning of fair representations. In: Proc. of the ICLR. 2020.
[38] Xu DP, Yuan SH, Zhang L, Wu XT. FairGAN: Fairness-aware generative adversarial networks. In: Proc. of the BigData. 2018.
570−575.
+
[39] Xu DP, Yuan SH, Zhang L, Wu XT. FairGAN : Achieving fair data generation and classification through generative adversarial
nets. In: Proc. of the BigData. 2019. 1401−1406.
[40] Xu DP, Wu YK, Yuan SH, Zhang L, Wu XT. Achieving causal fairness through generative adversarial networks. In: Proc. of the
IJCAI. 2019. 1452−1458.
[41] Kocaoglu M, Snyder C, Dimakis AG, Vishwanath S. CausalGAN: Learning causal implicit generative models with adversarial
training. In: Proc. of the ICLR (Poster). 2018.
[42] Creager E, Madras D, Jacobsen JH, Weis MA, Swersky K, Pitassi T, Zemel RS. Flexibly fair representation learning by
disentanglement. In: Proc. of the ICML. 2019. 1436−1445.
[43] Gordaliza P, Barrio E, Gamboa F, Loubes JM. Obtaining fairness using optimal transport theory. In: Proc. of the ICML. 2019.
2357−2365.
[44] Bechavod Y, Ligett K. Learning fair classifiers: A regularization-inspired approach. arXiv preprint arXiv:1707.00044v3, 2017.
[45] Chris R, Kusner MJ, Loftus JR, Silva R. When worlds collide: integrating different counterfactual assumptions in fairness. In: Proc.
of the NIPS. 2017. 6414−6423.
[46] Wu YK, Zhang L, Wu XT, Tong HH. PC-fairness: A unified framework for measuring causality-based fairness. In: Proc. of the
NeurIPS. 2019. 3399−3409.
[47] Wu YK, Zhang L, Wu XT. Counterfactual fairness: Unidentification, bound and algorithm. In: Proc. of the IJCAI. 2019.
1438−1444.