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POPL 2019
Sun 13 - Sat 19 January 2019 Cascais/Lisbon, Portugal

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 Jan

lafi-2019
09:00 - 10:30: LAFI (né PPS) - Archimedes at LAFI
lafi-201909:00 - 09:10
Talk
lafi-201909:10 - 09:30
Talk
Ugo Dal LagoUniversity of Bologna, Italy / Inria, France, Naohiko HoshinoKyoto University
lafi-201909:30 - 10:30
Talk
Matthijs VákárUniversity of Oxford
lafi-2019
11:00 - 12:30: LAFI (né PPS) - Borel at LAFI
lafi-201911:00 - 11:30
Talk
Adam ŚcibiorUniversity of Cambridge and MPI Tuebingen, Michael ThomasIndependent Researcher
lafi-201911:30 - 12:00
Talk
Eli SenneshNortheastern University, Adam ŚcibiorUniversity of Cambridge and MPI Tuebingen, Hao WuNortheastern University, Jan-Willem van de MeentNortheastern University
lafi-201912:00 - 12:30
Talk
lafi-2019
16:00 - 17:30: LAFI (né PPS) - Dirichlet at LAFI
lafi-201916:00 - 16:30
Talk
Simon Castellan, Hugo PaquetUniversity of Cambridge
lafi-201916:30 - 17:00
Talk
Steven HoltzenUniversity of California, Los Angeles, Joe QianUniversity of California, Los Angeles, Todd MillsteinUniversity of California, Los Angeles, Guy Van den BroeckUniversity of California, Los Angeles
lafi-201917:00 - 17:30
Talk
Matthew HeimerdingerIndiana University, Chung-chieh ShanIndiana University, USA

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.