LAFI 2021

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.

Accepted Papers

Call for Extended Abstracts (extended deadline)

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                 Call for Extended Abstracts

                          LAFI 2021
         POPL 2021 workshop on Languages for Inference 

                        January 17, 2021
          https://popl21.sigplan.org/home/lafi-2021

           Submission deadline on October 30, 2020 (extended).

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***** Submission Summary *****

Deadline: October 30, 2020 (extended) Link: https://lafi21.hotcrp.com/ Format: extended abstract (2 pages + references)

***** 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 modeling and inference;

• variational and differential modeling 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.

Two years ago, 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 *****

Submission deadline on October 30, 2020 (extended).

Submission link: https://lafi21.hotcrp.com/

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 program is not intended to preclude later formal publication.