Invited speaker
Fritz Obermeyer, Uber AI Labs
About LAFI
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:
Last year we explicitly expanded the focus of the workshop from statistical probabilistic programming to encompass differentiable programming for statistical machine learning. This change seemed well-received by the community, and we want to continue it this year in an effort to extend the strong ties between programming language-based machine learning and the POPL community.
Call for contributions, important dates, and the Program Committee are listed elsewhere on this page.
Tue 21 JanDisplayed time zone: Saskatchewan, Central America change
09:00 - 10:00 | |||
09:00 60mTalk | Invited talk: Nonstandard Interpretation in Pyro LAFI Pre-print | ||
10:30 - 12:30 | |||
10:30 30mTalk | A Differential-form Pullback Programming Language for Higher-order Reverse-mode Automatic Differentiation LAFI | ||
11:00 30mTalk | A Monad for Point Processes LAFI File Attached | ||
11:30 30mTalk | Denotational Semantics for Differentiable Programming with Manifolds LAFI Jesse Sigal University of Edinburgh | ||
12:00 30mTalk | Backpropagation in the Simply Typed Lambda-calculus with Linear Negation LAFI | ||
12:30 - 14:00 | |||
12:30 90mLunch | Lunch Catering | ||
14:00 - 15:05 | |||
14:00 30mTalk | Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support LAFI Yuan Zhou University of Oxford, Hongseok Yang KAIST, Yee Whye Teh University of Oxford, Tom Rainforth Department of Statistics, University of Oxford | ||
14:32 15mTalk | MetaPPL: Inference Algorithms as First-Class Generative Models LAFI Alexander K. Lew Massachusetts Institute of Technology, USA, Benjamin Sherman Massachusetts Institute of Technology, USA, Marco Cusumano-Towner MIT-CSAIL, Austin Garrett MIT, Ben Zinberg MIT, Vikash K. Mansinghka MIT, Michael Carbin Massachusetts Institute of Technology File Attached | ||
14:49 16mTalk | Monte Carlo Semantic Differencing of Probabilistic Programs LAFI | ||
15:35 - 17:45 | |||
15:35 30mTalk | Coinductive Trees for Exact Inference of Probabilistic Programs LAFI Alexander Bagnall Ohio University, Gordon Stewart Ohio University, Anindya Banerjee IMDEA Software Institute | ||
16:05 30mTalk | Name generation and Higher-order Probabilistic Programming (Or is new=rnd?) LAFI File Attached | ||
16:35 30mTalk | Density Functions of Statistical Probabilistic Programs LAFI | ||
17:05 30mTalk | Probabilistic Programming around Gaussian Processes LAFI David Tolpin PUB+ | ||
17:35 10mDay closing | Closing LAFI | ||
Unscheduled Events
| Not scheduled Talk | Gen and MetaGen LAFI Austin Garrett MIT, Alexander K. Lew Massachusetts Institute of Technology, USA, Benjamin Sherman Massachusetts Institute of Technology, USA, Ben Zinberg MIT, Michael Carbin Massachusetts Institute of Technology, Marco Cusumano-Towner MIT-CSAIL, Vikash Mansinghka MIT |
Accepted talks
Talks in alphabetical order:
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:
Last year we explicitly expanded the focus of the workshop from statistical probabilistic programming to encompass differentiable programming for statistical machine learning. This change seemed well-received by the community, and we want to continue it this year in an effort to extend the strong ties between programming language-based machine learning and the POPL community.
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 and differential 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 21 Jan 2020 Workshop |
| Wed 12 Dec 2012 - Tue 21 Jan 2020 Accepted talks announced. |
| Mon 2 Dec 2019 Author notification |
| Thu 31 Oct 2019 Submission deadline (extended) |
Justin Hsu
University of Wisconsin-Madison, USA
United States
Ohad KammarCo-chair
University of Edinburgh
United Kingdom
Jerzy Karczmarczuk
France
Marie Kerjean
Dougal MaclaurinCo-chair
Barak A. Pearlmutter
Maynooth University
Ireland
David Tolpin
PUB+
Israel
Andrea Walther
Humboldt-Universität zu Berlin
Germany
Richard Wei
Apple Inc.
United States
