[TYPES/announce] Call for Submissions: International Conference on Probabilistic Programming (PROBPROG 2021)

[Jean-Baptiste Tristan]

Call for Submissions: International Conference on Probabilistic Programming
(PROBPROG 2021)

Conference Dates: Wed Oct 20 - Fri Oct 22, 2021

Deadline: Thu May 6, 2021 11:59 PM AOE

Submissions: https://cmt3.research.microsoft.com/PROBPROG2021/

Instructions: https://probprog.cc/submissions/

The International Conference on Probabilistic Programming (PROBPROG 2021)
will take place  Oct 20 - 22, 2021, in a fully online format. The
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 at 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.

Program Chairs

- Guy Van den Broeck (UCLA)
- Lawrence Murray (Uber)

General Chairs

- Jean-Baptiste Tristan (Boston College)
- Jan-Willem van de Meent (Northeastern University)


Program Information

- Wed 20 October: Industry day and Tutorials
- Thu 21 October: Main conference
- Fri 22 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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