Practical Bayesian Nonparametrics

In theory, Bayesian nonparametric (BNP) methods are well suited to the large data sets that arise in the sciences, technology, politics, and other 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.

In practice, BNP methods present a number of computational and modeling challenges. Recent work has brought a wide range of models to bear on applied problems, going beyond the Dirichlet process and Gaussian process. Meanwhile, advances in accelerated inference are making these models tractable in big data problems.

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, e.g. Stan, BNPy, and BNP.jl.

We expect workshop participants to come from a variety of fields, including but not limited to machine learning, statistics, engineering, political science, and various 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 work that makes BNP methods easy-to-use in practice and computationally efficient. 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 challenges that need to be overcome in order to make BNP methods more widespread in practice. 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 the “Bayesian Nonparametrics: The Next Generation” workshop held at NIPS in 2015. While that workshop had a broad remit, spanning theory, applications and computation, this year’s workshop shows a fresh focus on the practical aspects of BNP methods. During last year’s panel discussion, there were many questions about computational techniques and practical applications, suggesting that this direction will be of great interest to the many applied machine learning researchers who attend the conference.