91 citations · 231 across the 17 of their papers we have counts for
4 papers · 1 filter
Bias Propagation in Federated Learning
Hongyan Chang, Reza Shokri
We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive…
Dual Compensation Residual Networks for Class Imbalanced Learning
Ruibing Hou, Hong Chang, Bingpeng Ma +2
Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution,…
On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel +1
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance b…
Diversity-Measurable Anomaly Detection
Wenrui Liu, Hong Chang, Bingpeng Ma +2
Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not wel…