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Learning fair models without sensitive attributes: A generative approach.

, , , and . Neurocomputing, (December 2023)

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You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features., , , and . CoRR, (2021)Learning fair models without sensitive attributes: A generative approach., , , and . Neurocomputing, (December 2023)Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network., , , , , and . CoRR, (2021)Learning Fair Graph Neural Networks With Limited and Private Sensitive Attribute Information., and . IEEE Trans. Knowl. Data Eng., 35 (7): 7103-7117 (July 2023)Unsupervised Image Super-Resolution with an Indirect Supervised Path., , , , , , , and . CoRR, (2019)FairGNN: Eliminating the Discrimination in Graph Neural Networks with Limited Sensitive Attribute Information., and . CoRR, (2020)Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series., and . ICLR, OpenReview.net, (2022)Unsupervised Image Super-Resolution with an Indirect Supervised Path., , , , , , , , , and . CVPR Workshops, page 1924-1933. Computer Vision Foundation / IEEE, (2020)Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features., , , and . WSDM, page 1433-1442. ACM, (2022)Towards Prototype-Based Self-Explainable Graph Neural Network., and . CoRR, (2022)