56 citations · 164 across the 37 of their papers we have counts for
5 papers · 1 filter
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan +3
Federated learning distributes data among clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tack…
Merging Beyond: Streaming LLM Updates via Activation-Guided Rotations
Yuxuan Yao, Haonan Sheng, Qingsong Lv +11
The escalating scale of Large Language Models (LLMs) necessitates efficient adaptation techniques. Model merging has gained prominence for its efficiency and controllability. Howev…
AROMA: Autonomous Rank-one Matrix Adaptation
Hao Nan Sheng, Zhi-yong Wang, Mingrui Yang +1
As large language models continue to grow in size, parameter-efficient fine-tuning (PEFT) has become increasingly crucial. While low-rank adaptation (LoRA) offers a solution throug…
Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers
Zhi-Yong Wang, Hing Cheung So, Abdelhak M. Zoubir
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm,…
Network Topology Inference with Sparsity and Laplacian Constraints
Jiaxi Ying, Xi Han, Rui Zhou +2
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of…