[TYPES/announce] LAFI 2019: Languages for Inference --- Final Call-for-Proposals

[Ohad Kammar]

LAFI 2019: Languages for Inference (formerly PPS)
================================================
Tuesday, 15 January 2019, Cascais/Lisbon, Portugal
A workshop affiliated with POPL 2019
https://popl19.sigplan.org/track/lafi-2019

Important dates (anywhere on earth)
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 LAFI submission deadline       Thu  1 Nov 2018
 Notification                   Mon  3 Dec 2018
 Early Registration Deadline    Thu 10 Dec 2018
 Workshop                       Tue 15 Jan 2019
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Submission: https://lafi19.hotcrp.com/
Registration: https://popl19.sigplan.org/attending/Registration

Context
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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

+ PPS-2018: http://conf.researchr.org/track/POPL-2018/pps-2018
  blog:     http://pps2018.soic.indiana.edu/

+ PPS-2017: http://conf.researchr.org/track/POPL-2017/pps-2017
  blog:     http://pps2017.soic.indiana.edu/)

+ PPS-2016: http://conf.researchr.org/track/POPL-2016/pps-2016
  blog:     http://pps2016.soic.indiana.edu/)

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.

Please submit them by November 1(AoE) using HotCRP at:
https://lafi19.hotcrp.com/

In line with the SIGPLAN Republication Policy:

http://www.sigplan.org/Resources/Policies/Republication/

inclusion of extended abstracts in the programme is not intended to
preclude later formal publication.

Programme committee:
Atılım Güneş Baydin        University of Oxford Department of Engineering
Bart van Merriënboer       University of Montreal
Christine Tasson           University Paris Diderot
David Duvenaud             University of Toronto
Jeffrey Siskind (co-chair) School of Electrical and Computer
                           Engineering, Purdue University
Matthew Johnson            Google Brain
Ohad Kammar     (co-chair) University of Oxford Department of
                           Computer Science
Praveen Narayanan          Indiana University
Ryan Culpepper             Czech Technical University
Sophia Gold                Tezos
Steven Holtzen             University of California Los Angeles
Tom Rainforth              University of Oxford Department of Statistics
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