25 citations · 37 across the 5 of their papers we have counts for
5 papers
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
Bochuan Cao, Yuanpu Cao, Lu Lin +1
Recently, Large Language Models (LLMs) have made significant advancements and are now widely used across various domains. Unfortunately, there has been a rising concern that LLMs c…
Source-Function Weighted-Transfer Learning for Nonparametric Regression with Seemingly Similar Sources
Lu Lin, Weiyu Li
The homogeneity, or more generally, the similarity between source domains and a target domain seems to be essential to a positive transfer learning. In practice, however, the simil…
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox
Lu Lin, Weiyu Li
A basic condition for efficient transfer learning is the similarity between a target model and source models. In practice, however, the similarity condition is difficult to meet or…
Communication-Efficient Adaptive Federated Learning
Yujia Wang, Lu Lin, Jinghui Chen
Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of fed…
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
Yujia Wang, Lu Lin, Jinghui Chen
Due to the explosion in the size of the training datasets, distributed learning has received growing interest in recent years. One of the major bottlenecks is the large communicati…