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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.

Plenary
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Tue 21 Jan
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10:00 - 10:30
Tuesday Morning BreakCatering at Break
12:30 - 14:00
Tuesday LunchCatering at Lunch Room
12:30
90m
Lunch
Lunch
Catering
14:00 - 15:05
14:00
30m
Talk
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
LAFI
Yuan ZhouUniversity of Oxford, Hongseok YangKAIST, Yee Whye TehUniversity of Oxford, Tom RainforthDepartment of Statistics, University of Oxford
14:32
15m
Talk
MetaPPL: Inference Algorithms as First-Class Generative Models
LAFI
Alexander K. LewMassachusetts Institute of Technology, USA, Benjamin ShermanMassachusetts Institute of Technology, USA, Marco Cusumano-TownerMIT-CSAIL, Austin GarrettMIT, Ben ZinbergMIT, Vikash MansinghkaMIT, Michael CarbinMassachusetts Institute of Technology
File Attached
14:49
16m
Talk
Monte Carlo Semantic Differencing of Probabilistic Programs
LAFI
15:05 - 15:35
Tuesday Afternoon BreakCatering at Break
15:35 - 17:45
15:35
30m
Talk
Coinductive Trees for Exact Inference of Probabilistic Programs
LAFI
Alexander BagnallOhio University, Gordon StewartOhio University, Anindya BanerjeeIMDEA Software Institute
16:05
30m
Talk
Name generation and Higher-order Probabilistic Programming (Or is new=rnd?)
LAFI
Dario SteinUniversity of Oxford, Sam StatonUniversity of Oxford, Michael WolmanMcGill University
File Attached
16:35
30m
Talk
Density Functions of Statistical Probabilistic Programs
LAFI
Tom MattinsonUniversity of Oxford, C.-H. Luke OngUniversity of Oxford
17:05
30m
Talk
Probabilistic Programming around Gaussian Processes
LAFI
17:35
10m
Day closing
Closing
LAFI

Not scheduled yet

Not scheduled yet
Talk
Gen and MetaGen
LAFI
Austin GarrettMIT, Alexander K. LewMassachusetts Institute of Technology, USA, Benjamin ShermanMassachusetts Institute of Technology, USA, Ben ZinbergMIT, Michael CarbinMassachusetts Institute of Technology, Marco Cusumano-TownerMIT-CSAIL, Vikash MansinghkaMIT

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.