Bayesian nonparametric (BNP) methods are well suited to the large data sets that arise in a wide variety of applied fields. By making use of infinite-dimensional mathematical structures, BNP methods allow 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 larger data sets. These properties have resulted in the adoption of BNP methods across a diverse set of application areas---including, but not limited to, biology, neuroscience, the humanities, social sciences, economics, and finance.

This workshop aims to highlight recent advances in modeling and computation through the lens of applied, domain-driven problems that require the infinite flexibility and interpretability of BNP. In this workshop, we will explore new BNP methods for diverse applied problems, including cutting-edge models being developed by application domain experts. We will also discuss the limitations of existing methods and discuss key problems that need to be solved. A major focus of the workshop will be to expose participants to practical software tools for performing Bayesian nonparametric analyses. In particular, we plan to host hands-on tutorials to introduce workshop participants to some of the software packages that can be used to easily perform posterior inference for BNP models. On the software panel, we will have researchers who have experience with BNP and development experience with popular software systems, such as TensorFlow, Edward, Stan, and Autograd.

We expect workshop participants to come from a variety of fields, including but not limited to machine learning, statistics, engineering, the social sciences, and biological sciences. The workshop will be relevant both to BNP experts as well as those interested in learning how to apply BNP models. There will be a special emphasis on novel application areas and computational developments that make BNP more accessible to the broader machine learning audience. Participants will leave the workshop with (i) exposure to recent advances in the field, (ii) hands-on experience with software implementing BNP methods, and (iii) an idea of the current major challenges in the field. These goals will be accomplished through a series of invited and contributed talks, a poster session, and at least one hands-on tutorial session where participants can get their hands dirty with BNP methods.

This workshop builds off of:
1. NIPS 2015: “Bayesian Nonparametrics: The Next Generation”: https://sites.google.com/site/nipsbnp2015/, and
2. NIPS 2016: “Practical Bayesian Nonparametrics”: https://sites.google.com/site/nipsbnp2016/,
which have spanned various areas of BNP, such as theory, applications and computation. This year’s workshop will have a fresh take on recent developments in BNP in connection to the broader range of research in statistics, machine learning, and application domains.

The 2018 workshop has received an endorsement from the International Society of Bayesian Analysis (ISBA) and sponsorship from Google.

Organizing Committee:
Diana Cai (Princeton)
Trevor Campbell (MIT/UBC)
Mike Hughes (Harvard/Tufts)
Tamara Broderick (MIT)
Nick Foti (U Washington)
Sinead Williamson (UT Austin)

Advisory Committee:
Emily Fox (U Washington)
Antonio Lijoi (Bocconi U)
Sonia Petrone (Bocconi U)
Igor Prünster (Bocconi U)
Erik Sudderth (UC Irvine)