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You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features.

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Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach., , , , , and . ICDM, page 1212-1217. IEEE Computer Society, (2018)Semi-Supervised Graph-to-Graph Translation., , , and . CoRR, (2021)GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks., , and . WSDM, page 833-841. ACM, (2021)Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features., , , and . WSDM, page 1433-1442. ACM, (2022)TopoImb: Toward Topology-Level Imbalance in Learning From Graphs., , , and . LoG, volume 198 of Proceedings of Machine Learning Research, page 37. PMLR, (2022)SCFormer: A Vision Transformer with Split Channel in Sitting Posture Recognition., , , and . MMM (1), volume 14554 of Lecture Notes in Computer Science, page 41-52. Springer, (2024)Disambiguated Node Classification with Graph Neural Networks., , and . WWW, page 914-923. ACM, (2024)A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability., , , , , , , and . CoRR, (2022)Explanation Guided Contrastive Learning for Sequential Recommendation., , , and . CIKM, page 2017-2027. ACM, (2022)Semi-Supervised Graph-to-Graph Translation., , , and . CIKM, page 1863-1872. ACM, (2020)