[TYPES/announce] [CFP] LAFI 2020: Languages for Inference --- Call-for-Participation

[KAMMAR Ohad]

tl;dr:
* List of talks and abstracts is out.
* Early registration deadline is Wednesday 18 Dec (TODAY).

LAFI 2020: Languages for Inference (formerly PPS)
================================================
Tuesday, 21 January 2020, New Orleans, Louisiana, US
A workshop affiliated with POPL 2020
https://popl20.sigplan.org/home/lafi-2020

Important dates (anywhere on earth)
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 Early registration deadline    Wed 18 Dec 2019 (TODAY)
 Workshop                       Tue 21 Jan 2020
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Registration: https://popl20.sigplan.org/attending/Registration

Invited speaker: Fritz Obermeyer, Uber AI Labs

    Nonstandard Interpretation in Pyro

https://popl20.sigplan.org/details/lafi-2020/1/Invited-talk-Nonstandard-Interpretation-in-Pyro

Accepted talks: https://popl20.sigplan.org/home/lafi-2020#event-overview

Context
=======

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

+ LAFI-2019: https://popl20.sigplan.org/track/lafi-2019

+ 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/)

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 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 involving ongoing work on
probabilistic and differentiable programming languages, semantics, and
systems.


Programme committee:
Justin Hsu,                   University of Wisconsin-Madison, USA
Ohad Kammar (co-chair)        University of Edinburgh, UK (co-chair)
Jerzy Karczmarczuk            France
Marie Kerjean                 Inria Nantes, France
Dougal Maclaurin (co-chair)   Google Brain, USA (co-chair)
Barak A. Pearlmutter          Maynooth University, Ireland
David Tolpin                  PUB+, Israel
Andrea Walther                Humboldt-Universität zu Berlin, Germany
Richard Wei                   Apple Inc., USA
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
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