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Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning.

, , and . AAAI, page 7859-7867. AAAI Press, (2022)

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Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning., , and . AAAI, page 7859-7867. AAAI Press, (2022)Overview and Importance of Data Quality for Machine Learning Tasks., , , , , , , , , and . KDD, page 3561-3562. ACM, (2020)Data Augmentation for Fairness in Personal Knowledge Base Population., , , , and . PAKDD (Workshops), volume 12705 of Lecture Notes in Computer Science, page 143-152. Springer, (2021)PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect., , , and . ICML, OpenReview.net, (2024)Ranking Data Slices for ML Model Validation: A Shapley Value Approach., , and . ICDE, page 1937-1942. IEEE, (2021)Gradient Coreset for Federated Learning., , , and . WACV, page 2636-2645. IEEE, (2024)Game of Gradients: Mitigating Irrelevant Clients in Federated Learning., and . AAAI, page 9046-9054. AAAI Press, (2021)Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing., , , and . AAAI, page 14397-14404. AAAI Press, (2024)Learning Recourse on Instance Environment to Enhance Prediction Accuracy., , , and . NeurIPS, (2022)