In theory, Bayesian nonparametric (BNP) methods are perfectly suited to the modern-day, large data sets that arise in the physical, natural, and social sciences, as well as in technology and the humanities. By making use of infinite-dimensional mathematical structures, Bayesian nonparametric statistics allows the complexity of a learned model to grow as the size of a data set grows---exhibiting desirable Bayesian regularization properties for small data sets and allowing the practitioner to learn ever more from data sets as they become larger.

This flexibility, however, presents both computational and modeling challenges. While there have been recent developments in accelerated inference for Bayesian nonparametric models, many approaches are not appropriate for large datasets. Further, while we have seen a growth in models for applied problems that move beyond the foundational Dirichlet and Gaussian processes, the widespread adoption of BNP methods has been limited in applied fields. In this workshop, we will address the modeling, theoretical, and computational challenges limiting adoption and how they can be circumvented. In particular, we will engage with applications specialists to better understand the best directions for BNP development as a tool for conducting applied research. We will explore computational tools for posterior inference algorithms that address the unique challenges of BNP methods including high/infinite-dimensionality and flexibility: e.g., MCMC, SMC, variational methods, and small-variance asymptotics to name a few. We will also consider the design and implementation of software to perform Bayesian nonparametric analyses, both for detailed use by experts in the field and for automatic use by researchers outside the field.

This workshop will bring together core researchers in BNP across a number of fields (machine learning, statistics, engineering, applied mathematics, etc.) with researchers working in a variety of application domains. We aim to focus on the next generation of BNP research by highlighting the contributions of younger researchers in the BNP community. We anticipate that participants will leave the workshop with (i) a foundation for understanding BNP methods, (ii) a perspective on recent advances in the field via a number of invited and contributed talks as well as poster presentations, and (iii) an idea of the challenges facing the field and future opportunities via talks and a panel discussion featuring experts both within and outside of the BNP community.