Divergence Measure

Divergence measures quantify the difference between probability distributions, a crucial task in numerous machine learning applications. Current research focuses on developing efficient measures for multiple distributions, particularly within deep learning frameworks like GANs and for knowledge distillation in large language models, often employing techniques like optimal transport and kernel methods. These advancements are improving the performance of algorithms in diverse areas such as image super-resolution, clustering, and domain adaptation, while also providing insights into human-AI collaboration and trust. The development of robust and computationally efficient divergence measures is thus vital for advancing machine learning methodologies and their practical applications.