Stein's method is a technique from probability theory for bounding the distance between probability measures using differential and difference operators. Although the method was initially designed as a technique for proving central limit theorems, it has recently caught the attention of the machine learning (ML) community and has been used for a variety of practical tasks. Recent applications include generative modeling, global non-convex optimisation, variational inference, de novo sampling, constructing powerful control variates for Monte Carlo variance reduction, and measuring the quality of (approximate) Markov chain Monte Carlo algorithms. Stein's method has also been used to develop goodness-of-fit tests and was the foundational tool in one of the NeurIPS 2017 Best Paper awards.

Although Stein's method has already had significant impact in ML, most of the applications only scratch the surface of this rich area of research in probability theory. There would be significant gains to be made by encouraging both communities to interact directly, and this inaugural workshop would be an important step in this direction. More precisely, the aims are: (i) to introduce this emerging topic to the wider ML community, (ii) to highlight the wide range of existing applications in ML, and (iii) to bring together experts in Stein's method and ML researchers to discuss and explore potential further uses of Stein's method.