The International Conference on Probabilistic Programming
The inaugural International Conference on Probabilistic Programming (PROBPROG) will be held in Boston, MA on Thursday October 4th, Friday Oct 5th, and Saturday October 6th.
Statistics and Data Analysis track:
Languages and Systems track
Practice of Probabilistic Programming track
University of Cambridge & Uber AI Labs
This conference is the only conference dedicated to both probabilistic programming research and the practice of probabilistic programming. It welcomes submissions for research presentations, demonstrations, open-source systems, and participants in open discussions with industry. It includes four tracks for invited and contributed presentations:
Probabilistic Programming and 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 human intelligence. 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. A central theme in this track is the use of probabilistic programming to integrate diverse approaches to knowledge representation, learning, and inference, including statistical, probabilistic, symbolic, and neural techniques.
Probabilistic Programming for Statistics and Data Analysis
Probabilistic programs and probabilistic programming technology for formulating and solving problems in statistics and in data analysis. Examples include applications, inference in latent variable models, parameter estimation, and automatic model discovery from data. This track also includes research that uses probabilistic programming to formulate and/or solve new problems at the intersection of programming languages, software engineering, data analysis, and statistics, such as model checking, model criticism, and verification, testing, analysis, and visualization of statistical models and statistical inference algorithms.
Probabilistic Programming Languages and Systems
The design, implementation, and formal modeling 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 empirical and theoretical study of the usability, performance, and accuracy of probabilistic programming languages and systems.
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.
This first year, we are focused on (i) creating a venue that can support the growth of probabilistic programming, in academia, industry, government, and the non-profit sector, and (ii) gathering a community that can provide rigorous peer review for research papers in future years.
Accordingly, we are soliciting the following kinds of submissions:
Original research in probabilistic programming, including research that is under review.
Highlights of already-published probabilistic programming research, that could be of interest to the PROBPROG community, or would benefit from PROBPROG feedback.
Work that discusses the design and implementation of probabilistic programming systems, or the application of probabilistic programs.
Submissions should be 2-page abstracts (exclusive of references), and should not be anonymized. Abstracts should use the ACM SIGPLAN format. Submissions will be lightly reviewed for technical correctness and topicality. Accepted submissions may be included in poster sessions, demos, or talk sessions.
Community feedback at PROGPROG 2018 will inform the PROBPROG Steering Committee’s decisions around when to create a peer-reviewed probabilistic programming journal and/or conference proceedings.
We are actively working to ensure that the PROBPROG conference is inclusive in the broadest sense. In particular, we encourage contributions from participants whose gender, sexual orientation, and/or ethnic identities are underrepresented in the field.
We welcome feedback from the community on policies and measures that help establish a venue that is welcoming to all participants. Please direct such suggestions and comments to email@example.com.