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.LG2023
Mahalanobis-Aware Training for Out-of-Distribution Detection
Connor Mclaughlin, Jason Matterer, Michael Yee
While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings. One critical task for safe de…