Density Ratio

Density ratio estimation focuses on quantifying the difference between two probability distributions, a crucial task across diverse fields like machine learning and statistics. Current research emphasizes improving the accuracy and stability of density ratio estimation, particularly in high-dimensional spaces and scenarios with limited data, employing techniques such as incremental mixtures, Bregman divergences, and kernel methods within various model architectures including neural networks and logistic regression. These advancements have significant implications for applications ranging from out-of-distribution detection and transfer learning to reinforcement learning and Bayesian optimization, enabling more robust and efficient algorithms in these areas.