17 citations · 126 across the 44 of their papers we have counts for
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cs.LG2026
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Arathi Nair M, J. Harshan, Anwitaman Datta
In federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to augment minority-class samples…
cs.LG2024
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
Zhiwei Zuo, Zhuo Tang, Bin Wang +2
New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledg…
cs.LG2024★ 1 cited
Machine unlearning through fine-grained model parameters perturbation
Zhiwei Zuo, Zhuo Tang, Kenli Li +1
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incu…