29 citations · 30 across the 3 of their papers we have counts for
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cs.LG2022★ 29 cited
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
Zeyu Tang, Jiji Zhang, Kun Zhang
Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections a…
cs.LG2022
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Yewen Fan, Nian Si, Kun Zhang
Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendati…
cs.LG2022★ 1 cited
Attainability and Optimality: The Equalized Odds Fairness Revisited
Zeyu Tang, Kun Zhang
Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have…