2 citations · 4 across the 13 of their papers we have counts for
9 papers · 1 filter
A Critical Review of Predominant Bias in Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predominant biases is crucial in advanci…
DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
Jiageng Zhu, Hanchen Xie, Jiazhi Li +1
Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various d…
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…
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…
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…
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representati…
Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those tw…
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength
Jiageng Zhu, Hanchen Xie, Wael AbdAlmageed
Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using…
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
Hanchen Xie, Mohamed E. Hussein, Aram Galstyan +1
Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of…