1 code implementation • 13 Nov 2023 • Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu, Hanchen Xie, Mohamed E. Hussein, Wael AbdAlmageed
To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation.
1 code implementation • 8 Oct 2023 • Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu, Hanchen Xie, Mohamed E. Hussein, Wael AbdAlmageed
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.
1 code implementation • 10 Aug 2023 • Jiageng Zhu, Hanchen Xie, Jianhua Wu, Jiazhi Li, Mahyar Khayatkhoei, Mohamed E. Hussein, Wael AbdAlmageed
Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling.
1 code implementation • 12 May 2023 • Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li, Mohamed E. Hussein, Wael AbdAlmageed
In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split.
no code implementations • 21 Sep 2022 • Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning.
no code implementations • 15 Sep 2022 • 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.
no code implementations • 3 Jun 2022 • Jiageng Zhu, Hanchen Xie, Wael AbdAlmageed
Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data.
no code implementations • 29 Sep 2021 • Jiageng Zhu, Hanchen Xie, Wael AbdAlmgaeed
Disentangled and invariant representation are two vital goals for representation learning and many approaches have been proposed to achieve one of them.