2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Similarity-Aware Mixture-of-Experts for Data-Efficient Continual Learning
Connor Mclaughlin, Nigel Lee, Lili Su
Machine learning models often need to adapt to new data after deployment due to structured or unstructured real-world dynamics. The Continual Learning (CL) framework enables contin…
cs.LG2024★ 2 cited
Personalized Federated Learning via Feature Distribution Adaptation
Connor J. Mclaughlin, Lili Su
Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, h…