2 citations · 2 across the 4 of their papers we have counts for
4 papers
Counterfactually Fair Representation
Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang
The use of machine learning models in high-stake applications (e.g., healthcare, lending, college admission) has raised growing concerns due to potential biases against protected s…
Loss Balancing for Fair Supervised Learning
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
Supervised learning models have been used in various domains such as lending, college admission, face recognition, natural language processing, etc. However, they may inherit pre-e…
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
Kun Jin, Tongxin Yin, Zhongzhu Chen +4
We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distribu…
Fairness and Accuracy under Domain Generalization
Thai-Hoang Pham, Xueru Zhang, Ping Zhang
As machine learning (ML) algorithms are increasingly used in high-stakes applications, concerns have arisen that they may be biased against certain social groups. Although many app…