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

	Title
	A weakest pre-expectation semantics for mixed-sign expectations Benjamin Lucien Kaminski, Joost-Pieter Katoen
	An application of computable distributions to the semantics of probabilistic programs: part 2 Daniel Huang, Greg Morrisett
	An exponential family basis for probabilistic programming Chad Scherrer
	Building inference algorithms from monad transformers Adam Ścibior, Yufei Cai, Klaus Ostermann, Zoubin Ghahramani
	Commutativity logic for probabilistic trace equivalence: complete or not? Paul Blain Levy, Nathan Bowler
	Efficient exact inference in discrete Anglican programs Robert Cornish, Frank Wood, Hongseok Yang
	Encapsulating models and approximate inference programs in probabilistic modules Marco Cusumano-Towner, Vikash Mansinghka
	Exchangeable random process and data abstraction Sam Staton, Hongseok Yang, Nathanael L. Ackerman, Cameron Freer, Daniel Roy
	GraPPa: spanning the expressivity vs. efficiency continuum Edwin Westbrook, Chad Scherrer, Nathan Collins, Eric Mertens
	Mathematical structures of probabilistic programming Ilias Garnier, Fredrik Dahlqvist, Florence Clerc, Vincent Danos
	Metropolis-Hastings for mixtures of conditional distributions Oleg Kiselyov
	On computable representations of exchangeable data Nathanael L. Ackerman, Jeremy Avigad, Cameron Freer, Daniel Roy, Jason M. Rute
	ProbLog and applicative probabilistic programming Alexander Vandenbroucke, Tom Schrijvers
	Probabilistic logic programs: unifying program trace and possible world semantics Angelika Kimmig, Luc De Raedt
	Probabilistic programming and a domain theoretic approach to Skorohod's theorem Michael Mislove
	Reasoning about inference in probabilistic programs Chandrakana Nandi, Adrian Sampson, Dan Grossman, Todd Mytkowicz, Kathryn S McKinley
	Reducing probabilistic choice to nondeterministic choice Ernie Cohen
	Support and influence analysis for visualizing posteriors of probabilistic programs Long Ouyang
	Synthetic topology in homotopy type theory for probabilistic programming Florian Faissole, Bas Spitters
	The extended semantics for probabilistic programming languages Siddharth Srivastava, Nicholas Hay, Yi WU, Stuart Russell
	The semantics of subroutines and iteration in the Bayesian programming language ProBT Raphaël Laurent, Kamel Mekhnacha, Emmanuel Mazer, Pierre Bessière
	Towards a metric semantics for probabilistic programming (invited talk) Gordon Plotkin

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