3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo +2
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertai…
cs.LG2023
A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier
Hua Tang, Lu Cheng, Ninghao Liu +1
While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this lon…
cs.LG2023
Fair Few-shot Learning with Auxiliary Sets
Song Wang, Jing Ma, Lu Cheng +1
Recently, there has been a growing interest in developing machine learning (ML) models that can promote fairness, i.e., eliminating biased predictions towards certain populations (…