Representation Function

Representation learning focuses on automatically discovering meaningful data features, enabling efficient and effective machine learning. Current research emphasizes understanding how transformer networks learn complex representations, particularly within the context of in-context learning and federated learning settings, often employing algorithms like alternating minimization-descent and differentially private methods. This area is crucial for improving the sample efficiency and privacy guarantees of machine learning models, with applications ranging from natural language processing to reinforcement learning and beyond. Theoretical advancements are actively addressing the challenges of analyzing representation learning in general function classes, moving beyond simpler linear models.