Recent Applications of Stein's Method in Machine Learning

Abstract: Stein's method is a powerful technique for deriving fundamental theoretical results on approximating and bounding distances between probability measures, such as central limit theorem. Recently, it was found that the key ideas in Stein's method, despite being originally designed as a pure theoretical technique, can be repurposed to provide a basis for developing practical and scalable computational methods for learning and using large scale, intractable probabilistic models. This talk will give an overview for some of these recent advances of Stein's method in machine learning.