LAFI 2027

The Languages for Inference (LAFI) workshop aims to gather programming language researchers interested in machine learning and statistical methods, to advance all aspects of languages for inference: languages that offer built-in support for expressing probabilistic or differentiable models, and methods for inference and optimization over them, as programs, to ease reasoning, use, and reuse.

Topics include but are not limited to:

• Design of programming languages for probabilistic inference 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 differentiable modeling and inference
• Semantics (axiomatic, operational, denotational, games, etc.) and types for probabilistic and differentiable programming
• Categorical methods in the theory of probabilistic and differentiable programming
• Formal verification of probabilistic programs and probabilistic inference
• Applications of probabilistic and differentiable programming

Call for Extended Abstracts

We invite the submission of extended abstracts (2 pages + references + optional appendices) to the Languages for Inference (LAFI) workshop, colocated with POPL 2027.

LAFI aims to gather programming language researchers interested in machine learning and statistical methods, to advance all aspects of languages for inference. Topics include but are not limited to:

• Design of programming languages for probabilistic inference 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 differentiable modeling and inference
• Semantics (axiomatic, operational, denotational, games, etc.) and types for probabilistic and differentiable programming
• Categorical methods in the theory of probabilistic and differentiable programming
• Formal verification of probabilistic programs and probabilistic inference
• Applications of probabilistic and differentiable programming

Dissemination of research. The workshop is informal, and our goal is to foster collaboration and establish a shared foundation for research on languages for inference. 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.

Format. Uploads must be in PDF. Although no specific format is required, we suggest using a conference template (either single- or double-column) in review mode, including line number annotations that reviewers can refer to when giving feedback.

Page limit. 2 pages of main content, unlimited number of references and appendices. (Please note that reviewers are not required or expected to read appendices.)

Anonymity. Submissions should be anonymized for peer review.

In line with the SIGPLAN Republication Policy, inclusion of extended abstracts in the program should not preclude later formal publication.

We strive to create an inclusive environment that does not demand traveling for presenters or participants.