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
Tue 17 Jan Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
09:00 - 10:00 | |||
09:00 60mTalk | Towards a metric semantics for probabilistic programming (invited talk) PPS |
10:30 - 12:00 | |||
10:30 20mTalk | An application of computable distributions to the semantics of probabilistic programs: part 2 PPS | ||
10:50 10mMeeting | Discussion 1 PPS | ||
11:00 20mTalk | Probabilistic programming and a domain theoretic approach to Skorohod's theorem PPS Michael MisloveTulane | ||
11:20 10mMeeting | Discussion 2 PPS | ||
11:30 20mTalk | Building inference algorithms from monad transformers PPS Adam ŚcibiorUniversity of Cambridge, Yufei CaiUniversity of Tübingen, Germany, Klaus OstermannUniversity of Tübingen, Germany, Zoubin GhahramaniUniversity of Cambridge | ||
11:50 10mMeeting | Discussion 3 PPS |
14:00 - 15:30 | |||
14:00 20mTalk | Commutativity logic for probabilistic trace equivalence: complete or not? PPS | ||
14:20 10mMeeting | Discussion 4 PPS | ||
14:30 20mTalk | Mathematical structures of probabilistic programming PPS Ilias GarnierUniversity of Edinburgh, Fredrik DahlqvistUniversity College London, Florence ClercMcGill University, Vincent DanosENS Paris/CNRS | ||
14:50 10mMeeting | Discussion 5 PPS | ||
15:00 20mTalk | A weakest pre-expectation semantics for mixed-sign expectations PPS | ||
15:20 10mMeeting | Discussion 6 PPS |
16:30 - 18:00 | |||
16:30 20mTalk | An exponential family basis for probabilistic programming PPS Chad ScherrerGalois, Inc. | ||
16:50 10mMeeting | Discussion 7 PPS | ||
17:00 20mTalk | The semantics of subroutines and iteration in the Bayesian programming language ProBT PPS | ||
17:20 10mMeeting | Discussion 8 PPS | ||
17:30 20mTalk | Exchangeable random process and data abstraction PPS Sam StatonUniversity of Oxford, Hongseok YangUniversity of Oxford, Nathanael L. AckermanHarvard University, Cameron FreerGamalon and Borelian, Daniel Roy | ||
17:50 10mMeeting | Discussion 9 PPS |
18:15 - 19:15 | |||
18:15 20mTalk | Reducing probabilistic choice to nondeterministic choice PPS Ernie CohenAmazon Web Services | ||
18:35 10mMeeting | Discussion 10 PPS | ||
18:45 20mTalk | GraPPa: spanning the expressivity vs. efficiency continuum PPS Edwin WestbrookGalois, Inc., Chad ScherrerGalois, Inc., Nathan CollinsGalois, Inc., Eric MertensGalois, Inc. | ||
19:05 10mMeeting | Discussion 11 PPS |