1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising meth…
cs.LG2023★ 1 cited
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising met…
cs.LG2023
Shadow Datasets, New challenging datasets for Causal Representation Learning
Jiageng Zhu, Hanchen Xie, Jianhua Wu +4
Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which i…