R\'enyi Entropy

Rényi entropy, a generalization of Shannon entropy, is a measure of uncertainty or information content applicable to various probability distributions. Current research focuses on its application in diverse fields, including improving the efficiency of sampling algorithms (e.g., through constrained proximal samplers), developing quantum algorithms for entropy estimation, and enhancing machine learning models by incorporating Rényi entropy-based constraints for fairness and generalization. This versatile measure is proving valuable for advancing theoretical understanding in information theory and for developing practical improvements in areas such as deep learning, privacy-preserving data analysis, and reinforcement learning.