2026
On the Power of Source Screening for Learning Shared Feature Extractors
ICML 2026spotlight
Learning with shared representation is widely recognized as an effective way to separate commonalities from heterogeneity across various heterogeneous sources. Most existing work includes all related data sources via simultaneously training a common feature extractor and source-specific heads. It is…