Workshop on probabilistic programming semantics

Probabilistic programming is the idea of expressing probabilistic models and inference methods as programs, to ease use and reuse. The recent rise of practical implementations as well as research activity in probabilistic programming has renewed the need for semantics to help us share insights and innovations.

This workshop aims to bring programming-language and machine-learning researchers together to advance the semantic foundations of probabilistic programming. Topics include but are not limited to:

• the denotational semantics of probabilistic functions, open universe, loops, and conditioning;
• the operational semantics of sampling, exact inference, and MCMC transitions;
• axiomatic and equational reasoning;
• types and polymorphism;
• and last but not least, how semantics informs any aspect of probabilistic programming, be it design, theory, implementation, or applications.

Accepted Presentations

Call for extended abstracts

We expect this workshop to be informal, and our goal is to foster collaboration and establish common ground. Thus, the proceedings will not be a formal or archival publication, and we expect to spend only a portion of the workshop day on traditional research talks. Nevertheless, as a concrete basis for fruitful discussions, we call for extended abstracts describing specific and ideally ongoing work on probabilistic programming semantics.

Extended abstracts are up to 2 pages in PDF format. Please submit them by October 31 using EasyChair: https://easychair.org/conferences/?conf=pps2017