6 citations · 11 across the 5 of their papers we have counts for
5 papers · 1 filter
Poisoning Attacks on Fair Machine Learning
Minh-Hao Van, Wei Du, Xintao Wu +1
Both fair machine learning and adversarial learning have been extensively studied. However, attacking fair machine learning models has received less attention. In this paper, we pr…
Fair Regression under Sample Selection Bias
Wei Du, Xintao Wu, Hanghang Tong
Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the r…
Robust Fairness-aware Learning Under Sample Selection Bias
Wei Du, Xintao Wu
The underlying assumption of many machine learning algorithms is that the training data and test data are drawn from the same distributions. However, the assumption is often violat…
Fairness-aware Agnostic Federated Learning
Wei Du, Depeng Xu, Xintao Wu +1
Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about…
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
Depeng Xu, Wei Du, Xintao Wu
When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r.t. each group. Gradient clipping and random noise addition disproportionate…