Relative Entropy

Relative entropy, also known as Kullback-Leibler divergence, quantifies the difference between two probability distributions, serving as a crucial tool in various fields. Current research focuses on improving its estimation and application in diverse areas, including neural network training (e.g., using relative entropy regularization or as a loss function), information bottleneck methods, and dimensionality reduction techniques (e.g., t-SNE variants). These advancements enhance the accuracy and efficiency of algorithms across machine learning, information theory, and signal processing, impacting areas such as generative modeling, neural architecture search, and network flow optimization. The development of novel algorithms and theoretical frameworks for handling high-dimensional data and improving the computational tractability of relative entropy calculations are key themes.