[TYPES/announce] PROBPROG 2020 Call for Late-breaking Submissions

[Jean-Baptiste Tristan]

Conference Dates:  Oct 22 - 24 2020 (previously Apr 23 – 25 2020)
Deadline: Mon Sep 14, 2020 11:59 PM AOE
Submissions: https://cmt3.research.microsoft.com/PROBPROG2020
Instructions: https://probprog.cc/submissions/

The International Conference on Probabilistic Programming (PROBPROG 2020)
has been rescheduled to Oct 22 - 24 and will take place in a fully online
format. Owing to the 5-month gap relative to the original conference
dates,  we would like to invite authors who did not have a chance to submit
in January to do so in September.

The PROBPROG 2020 conference invites two kinds of submissions:

1. Extended Abstracts: Authors may submit work in the form of an extended
abstract of 2-6 pages for consideration for a poster presentation, talk, or
full-length proceedings submission. Extended abstracts are intended as a
mechanism for discussing work that may be preliminary, and for this reason,
are non-archival. Titles for accepted poster presentations and talks will
be listed on the conference website. Our aim is to enable researchers to
get feedback from the PROBPROG community that helps mature the research,
strengthen the probabilistic programming content, and improve the chances
of acceptance in top venues aligned with other fields.

2. Syndicated Submissions: Authors may submit work that has been accepted
for publication in another venue within the last 12 months for
consideration as a poster presentation or talk. These submissions may be
full-length and are also non-archival, but will be listed on the conference
website.
*Note: *Authors of previously accepted submissions need to resubmit this
cycle. Authors of submissions that were not accepted are encouraged to
resubmit an updated abstract.

Organizers

   -

   Vikash Mansinghka <http://probcomp.csail.mit.edu/principal-investigator/>
   (MIT)
   - Jean-Baptiste Tristan <http://jtristan.github.io/> (Boston College)
   - Jan-Willem van de Meent <http://www.ccs.neu.edu/home/jwvdm/>
   (Northeastern University)
   -

   Avi Pfeffer <https://www.linkedin.com/in/avi-pfeffer-03188025/> (Charles
   River Analytics)

Program Information

- Thursday 22 October: Industry day and Tutorials
- Friday 23 October: Main conference
- Saturday 24 October: Main conference

Probabilistic programming is an emergent field based on the idea that
probabilistic models can be efficiently represented as executable code.
This idea has enabled researchers to formalize, automate, and scale-up many
aspects of modeling and inference; to make modeling and inference
accessible to a broader audience of developers and domain experts; and to
develop new programmable AI systems that integrate modeling and inference
approaches from multiple domains.

PROBPROG is the first international conference dedicated to probabilistic
programming. PROBPROG includes presentations on basic research, applied
research, open-source, and the practice of probabilistic programming.
PROBPROG attendees come from academia, industry, non-profits, and
government. The conference aims to achieve three goals:

1. Create a venue where researchers from multiple fields — e.g. programming
languages, statistics, machine learning, and artificial intelligence — can
meet, interact, and exchange ideas.
2. Grow a diverse and inclusive probabilistic programming community, by
actively seeking participation from under-represented groups, and providing
networking opportunities, mentorship, and feedback to all members.
3. Support the development of the practice of probabilistic programming,
including open-source systems and real-world applications, and provide a
bridge between the practice of probabilistic programming and basic research.
PROBPROG welcomes abstract submissions for contributed research
presentations, demonstrations, open-source systems, participants in open
discussions, and consideration for invited publication in an online
journal. Submissions should indicate alignment with one or more of the
following themes:

1. Artificial and Natural Intelligence. Probabilistic programs and
probabilistic programming technology for formulating and solving the core
problems of intelligence, including research relevant for engineering
artificial intelligence and for reverse-engineering natural intelligence. A
central theme in this track is new AI architectures based on probabilistic
programming that integrate statistical, symbolic, neural, Bayesian, and
simulation-based approaches to knowledge representation and learning.
Another central theme is proposals for learning probabilistic programs from
data, and modeling high-level forms of human learning using probabilistic
program synthesis. This track also includes research at the intersection of
probabilistic programming and intelligence augmentation, collective
intelligence, machine learning, and the development and analysis of
intelligent infrastructure.

2. Statistics and Data Analysis. Probabilistic programs and probabilistic
programming technology for formulating and solving problems in statistics
and data analysis. Topics include latent variable models, parameter
estimation, automated data modeling, Bayesian inference, calibration, model
checking, model criticism, visualization, and testing of statistical models
and inference algorithms. This track also includes statistical applications
and deployments of probabilistic programming for data analysis.

3. Languages, Tools, and Systems. The design, implementation, and formal
semantics of probabilistic programming languages and systems, including
domain-specific and general-purpose languages, interpreters, compilers,
probabilistic meta-programming techniques, probabilistic meta-programming
languages, and runtime systems. This track also includes research on
dynamic and static analysis of probabilistic programs, and empirical and
theoretical study of the usability, performance, and accuracy of
probabilistic programming languages and systems.
4. The Practice of Probabilistic Programming. This track is centered on
four themes: (i) probabilistic programs and systems based on probabilistic
programming that solve problems in industry, government, philanthropic
work, applied research, and teaching, as well as potential use cases for
probabilistic programs or probabilistic programming technology in these
areas; (ii) challenges that arise when using probabilistic programming in
practice, including inspection, debugging, testing, and performance
engineering; (iii) human-centric design of probabilistic programs and
probabilistic programming technology; and (iv) probabilistic programming
tools, probabilistic program analyses, probabilistic programming
styles/workflows, probabilistic programming practices/guidelines/experience
reports, and probabilistic programming environments with the potential to
address issues faced by practitioners.
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