Abstract

Probabilistic programming systems aim to accelerate iterative development of machine learning approaches by introducing an abstraction boundary: models are defined using a domain-specific language, and a back end implements generic inference methods for such programs. The aim of this research endeavor is to do for the domains of data science and artificial intelligence what compiler technologies have done for software development: enable practitioners to reason about their models at a higher level of abstraction.

In this talk I will discuss inference strategies employed in Anglican, a probabilistic programing system closely integrated with the language Clojure. Anglican has pioneered inference techniques based on sequential Monte Carlo that apply to programs written in general-purpose languages that support recursion, higher-order functions, and black box deterministic primitives. In addition to strategies for posterior inference, I will discuss extensions to policy search and marginal MAP estimation problems.

BIO

Jan-Willem is post-doc in Machine Learning at the Department of Engineering Science at Oxford. He works primarily on the Anglican probabilistic programming system, which he co-created with Frank Wood and David Tolpin. His broader research agenda is to understand how programs may be used to define structured and composable models for machine learning and artificial intelligence. To facilitate this agenda, he also works on inference techniques for probabilistic programs.