most citedInformation-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks

1 citations · 3 across the 7 of their papers we have counts for

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7 papers

cs.LG2024

ManiFPT: Defining and Analyzing Fingerprints of Generative Models

Hae Jin Song, Mahyar Khayatkhoei, Wael AbdAlmageed

Recent works have shown that generative models leave traces of their underlying generative process on the generated samples, broadly referred to as fingerprints of a generative mod…

cs.CV20241 cited

Exploring Perceptual Limitation of Multimodal Large Language Models

Jiarui Zhang, Jinyi Hu, Mahyar Khayatkhoei +2

Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perc…

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.LG20231 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…

cs.LG2023

Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions

Mahyar Khayatkhoei, Wael AbdAlmageed

Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models. Given their cen…