Nonconvex Optimization for Regression with Fairness Constraints

By Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao. 2018

111 citations recorded by the source corpus.

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Citations in the corpus

Wasserstein Fair Classification (2019, background): “Other work similar in spirit adds penalties to the objective (Donini et al., 2018; Komiyama et al., 2018).”
Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Calders et al. (2013), Johnson et al. (2016) and Komiyama et al. (2018) consider a more limited form of independence, expressed via a small number of moment constraints, such as lack of correlation, and design speciﬁc algorithms for linear least squares.”
Addressing multiple metrics of group fairness in data-driven decision making (2020, methodology): “…diﬀerent fairness notions lead to diﬀerent fairness criteria, and not all notions can be fulﬁlled with just one criterion, algorithm developers are left with the dilemma of deciding between diﬀerent value-concepts when trying to implement an appropriate fairness metric into the algorithm [21].”
Fairness in Machine Learning (2020, methodology): “…34, 37, 38, 59, 97, 135, 183, 185] or add penalties to the objective [5, 14, 22, 44, 46, 56, 58, 62, 73, 74, 75, 79, 82, 87, 88, 89, 93, 94, 95, 98, 104, 115, 119, 123, 133, 134, 135, 138, 139, 154, 165, 176, 179, 180, 182], others use adversary techniques to maximize the system ability to predict…”
Pairwise Fairness for Ranking and Regression (2019, background): “[21] controlled the correlation of the model output with protected variables (which may be continuous).”
Fairness in Machine Learning: A Survey (2020, methodology): “[171] include fairness perspectives in non-convex optimization for (linear) regression using the coefficient of determination”
On the Global Optima of Kernelized Adversarial Representation Learning (2019, background): “[15] consider the problem of enforcing fairness constraints in linear regression and provide a solution to obtain the global optima of the resulting non-convex problem.”
Identifying and Correcting Label Bias in Machine Learning (2019, background): “There is also recent work similar in spirit which encourages fairness by adding penalties to the objective; e.g, Donini et al. (2018) studies this for kernel methods and Komiyama et al. (2018) for linear models.”