Probability Divergence

Probability divergence measures quantify the difference between probability distributions, serving as crucial tools in various machine learning tasks and statistical inference. Current research focuses on developing new divergence measures with improved robustness to outliers and computational efficiency, particularly for high-dimensional data and data residing on manifolds, often employing techniques like kernel methods, normalizing flows, and optimal transport. These advancements are impacting fields like generative modeling, active learning, and privacy-preserving data analysis by enabling more accurate and reliable estimations of distributional differences, leading to improved model performance and more robust statistical inferences.