Inference concerns re-calibrating program parameters based on observed data, and has gained wide traction in machine learning and data science. Inference can be driven by probabilistic analysis and simulation, and through back-propagation and differentiation. Languages for inference offer built-in support for expressing probabilistic models and inference methods as programs, to ease reasoning, use, and reuse. The recent rise of practical implementations as well as research activity in inference-based 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 all aspects of languages for inference. Topics include but are not limited to:
- design of programming languages for inference and/or differentiable programming;
- inference algorithms for probabilistic programming languages, including ones that incorporate automatic differentiation;
- automatic differentiation algorithms for differentiable programming languages;
- probabilistic generative modelling and inference;
- variational and differential modelling and inference;
- semantics (axiomatic, operational, denotational, games, etc) and types for inference and/or differentiable programming;
- efficient and correct implementation;
- and last but not least, applications of inference and/or differentiable programming.
For a sense of the talks, posters, and blogs in past years, see:
This year we are explicitly expanding the focus of the workshop from statistical probabilistic programming to encompass differentiable programming for statistical machine learning.
Call for contributions, important dates, and the Program Committee are listed elsewhere on this page.
Tue 15 JanDisplayed time zone: Belfast change
09:00 - 10:30 | |||
09:00 30mTalk | Probabilistic Lambda Calculus: Beyond Deterministic Evaluation LAFI File Attached | ||
09:30 60mTalk | Invited talk: Connecting Probabilistic Programming Theory to Applications in Stan LAFI Matthijs Vákár University of Oxford File Attached | ||
11:00 - 12:30 | |||
11:00 30mTalk | The Geometry of Bayesian Programming LAFI | ||
11:30 30mTalk | Model and Inference Combinators for Deep Probabilistic Programming LAFI Eli Sennesh Northeastern University, Adam Ścibior University of Cambridge and MPI Tuebingen, Hao Wu Northeastern University, Jan-Willem van de Meent Northeastern University File Attached | ||
12:00 30mTalk | Server-side Probabilistic Programming LAFI David Tolpin PUB+ Media Attached | ||
14:00 - 15:30 | |||
14:00 30mTalk | A Nuts-and-Bolts Differential Geometric Perspective on Automatic Differentiation LAFI Barak A. Pearlmutter Maynooth University | ||
14:30 30mTalk | Kotlin∇: Differentiable Functional Programming with Algebraic Data Types LAFI Breandan Considine Université de Montréal File Attached | ||
15:00 30mTalk | Probabilistic Programming with CuPPL LAFI | ||
16:00 - 17:30 | |||
16:00 30mTalk | Probabilistic Programming Inference via Intensional Semantics LAFI | ||
16:30 30mTalk | Factorized Exact Inference for Discrete Probabilistic Programs LAFI Steven Holtzen University of California, Los Angeles, Joe Qian University of California, Los Angeles, Todd Millstein University of California, Los Angeles, Guy Van den Broeck University of California, Los Angeles | ||
17:00 30mTalk | Verified Equational Reasoning on a Little Language of Measures LAFI | ||
Unscheduled Events
| Not scheduled Talk | Strongly Typed Tracing of Probabilistic Programs (cancelled, alas) LAFI |
Accepted talks
Call for Extended Abstracts
Inference concerns re-calibrating program parameters based on observed data, and has gained wide traction in machine learning and data science. Inference can be driven by probabilistic analysis and simulation, and through back-propagation and differentiation. Languages for inference offer built-in support for expressing probabilistic models and inference methods as programs, to ease reasoning, use, and reuse. The recent rise of practical implementations as well as research activity in inference-based 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 all aspects of languages for inference. Topics include but are not limited to:
- design of programming languages for inference and/or differentiable programming;
- inference algorithms for probabilistic programming languages, including ones that incorporate automatic differentiation;
- automatic differentiation algorithms for differentiable programming languages;
- probabilistic generative modelling and inference;
- variational and differential modelling and inference;
- semantics (axiomatic, operational, denotational, games, etc) and types for inference and/or differentiable programming;
- efficient and correct implementation;
- and last but not least, applications of inference and/or differentiable programming.
For a sense of the talks, posters, and blogs in past years, see:
This year we are explicitly expanding the focus of the workshop from statistical probabilistic programming to encompass differentiable programming for statistical machine learning.
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 languages, semantics, and systems.
Submission guidelines
Extended abstracts are up to 2 pages in PDF format, excluding references.
In line with the SIGPLAN Republication Policy, inclusion of extended abstracts in the programme is not intended to preclude later formal publication.
Important dates and the Program Committee are listed elsewhere on this page.
| Tue 15 Jan 2019 Workshop |
| Mon 10 Dec 2018 Early registration deadline |
| Mon 3 Dec 2018 Author notification |
| Tue 6 Nov 2018 Submission deadline |
Bart Committee Member
Google Brain
Atılım Güneş BaydinCommittee Member
Department of Engineering Science, University of Oxford
Ryan CulpepperCommittee Member
Czech Technical University
Czechia
David Kristjanson DuvenaudCommittee Member
University of Toronto
Sophia GoldCommittee Member
Tezos
Steven HoltzenCommittee Member
University of California, Los Angeles
United States
Matthew J. JohnsonCommittee Member
Google Brain
Ohad KammarCo-chair
University of Edinburgh
United Kingdom
Praveen NarayananCommittee Member
Indiana University, USA
Tom RainforthCommittee Member
Department of Statistics, University of Oxford
Jeffrey Mark SiskindCo-chair
School of Electrical and Computer Engineering, Purdue University
Christine TassonCommittee Member
University Paris Diderot
