2 citations · 7 across the 5 of their papers we have counts for
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To be Robust and to be Fair: Aligning Fairness with Robustness
Junyi Chai, Xiaoqian Wang
Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not ye…
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang +3
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have…
Group-Aware Threshold Adaptation for Fair Classification
Taeuk Jang, Pengyi Shi, Xiaoqian Wang
The fairness in machine learning is getting increasing attention, as its applications in different fields continue to expand and diversify. To mitigate the discriminated model beha…
Shapley Explanation Networks
Rui Wang, Xiaoqian Wang, David I. Inouye
Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on post-hoc Shapley explanations, which can be comp…