Kullback Leibler Divergence

Kullback-Leibler (KL) divergence is a measure of the difference between two probability distributions, frequently used to quantify the information lost when one distribution is approximated by another. Current research focuses on improving KL divergence-based methods for various machine learning tasks, including generative modeling (e.g., using diffusion models and variational autoencoders), density estimation, and knowledge distillation, often incorporating techniques like Wasserstein gradient flows and $f$-divergences for enhanced performance and robustness. The accurate and efficient computation of KL divergence, and its generalizations, is crucial for advancing these fields, impacting applications ranging from image generation and natural language processing to Bayesian inference and statistical modeling.