Inverse Divergence

Inverse divergence, encompassing various measures like Bregman and Cauchy-Schwarz divergences, quantifies the difference between probability distributions, serving as a crucial tool in diverse machine learning tasks such as domain adaptation and clustering. Current research focuses on developing efficient estimators for these divergences, particularly for high-dimensional data and multiple distributions, often employing kernel methods and variational approaches within frameworks like variational autoencoders. These advancements improve model generalization, enhance the robustness of algorithms, and provide valuable insights into the discrepancies between human perception/cognition and machine learning models across various domains, including image processing, natural language processing, and reinforcement learning.