1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Domain-Skewed Federated Learning with Feature Decoupling and Calibration
Huan Wang, Jun Shen, Jun Yan +1
Federated learning (FL) allows distributed clients to collaboratively train a global model in a privacy-preserving manner. However, one major challenge is domain skew, where client…
cs.CL2026
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
Mingluo Su, Huan Wang
Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One…
cs.LG2023★ 1 cited
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Bingchang Liu, Chaoyu Chen, Cong Liao +9
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…