Lin Yang

About

Lin Yang is an Assistant Professor at the University of California Los Angeles. His research areas include reinforcement learning, federated learning, and contextual bandits, with recent articles focusing on topics such as the modeling of Bellman-error, distributed algorithms, and efficient policy learning. Additionally, he has explored the application of graph neural networks to enhance learning processes. He is dedicated to advancing the field through innovative computational methods and theoretical insights.