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cs.LG2026
Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
Dongyue Wu, Zilin Guo, Xiaoyu Li +4
The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning~(DP) methods which retain only a subset of informative sampl…
cs.LG2025
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis
Nannan Wu, Zengqiang Yan, Nong Sang +2
Training a model that effectively handles both common and rare data-i.e., achieving performance fairness-is crucial in federated learning (FL). While existing fair FL methods have…
cs.LG2025
CTR-Driven Advertising Image Generation with Multimodal Large Language Models
Xingye Chen, Wei Feng, Zhenbang Du +16
In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily f…