KL Divergence

Kullback-Leibler (KL) divergence quantifies the difference between two probability distributions, serving as a crucial tool in various machine learning applications. Current research focuses on refining KL divergence's use in areas like reinforcement learning (mitigating reward misspecification), knowledge distillation (improving efficiency and accuracy in transferring knowledge between models), and Bayesian neural networks (achieving well-defined variational inference). These advancements are improving model training, uncertainty quantification, and anomaly detection, impacting fields ranging from natural language processing to robotics and causal inference.