3 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.LG2026
On the Power of Source Screening for Learning Shared Feature Extractors
Leo Muxing Wang, Connor Mclaughlin, Lili Su
Learning with shared representation is widely recognized as an effective way to separate commonalities from heterogeneity across various heterogeneous sources. Most existing work i…
cs.LG2024
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…