Teacher Selection

Teacher selection in machine learning focuses on optimizing the transfer of knowledge from a "teacher" model to a "student" model, aiming to improve student performance and efficiency. Current research emphasizes techniques like knowledge distillation, exploring various methods for weighting and combining predictions from multiple teachers, including those with varying levels of confidence or expertise, and addressing issues like teacher-student distribution mismatch. These advancements are significant because they improve the efficiency and robustness of training complex models, impacting diverse applications from natural language processing to computer vision and reinforcement learning.