Call for Papers: Special Issue on "Probabilistic Programming" of
ACM Transactions on Probabilistic Machine Learning (ACM-TOPML)
Probabilistic programming is a very active research area that brings
together diverse fields, such as statistics, machine learning and AI,
applications, programming languages, and formal verification. The aim of
this special issue is to present new approaches, techniques, tools,
theories and experience reports about adopting, creating, applying and
improving probabilistic programming.
Topics of interest include, but are not limited to:
• Applications of probabilistic programming
• Approximate inference algorithms for probabilistic programs
• Automatic differentiation for probabilistic programs
• Automated program analysis for probabilistic programs
• Deep probabilistic programming languages
• Design and implementation of probabilistic programming languages
• Differentiable programming
• Exact inference algorithms for probabilistic programs
• Model learning and checking for probabilistic programs
• Statistical theory on inference schemes
• Semantics for probabilistic programming
• Synthesis and learning of probabilistic programs
• Theoretical analysis of probabilistic programs
• Types for probabilistic programming and differentiable programming
• Verification and testing probabilistic programming paradigms
Deadline for submissions: May 1, 2024.
Notification to authors: September 1, 2024
Submission Information:
The call for this special issue is an open call. All submitted papers will undergo
a rigorous peer-review process and should adhere to the general principles of
ACM Transactions on Probabilistic Machine Learning. Submissions should be
prepared according to the Author Guidelines:
Submitted papers must be original, must not have been previously published,
or be under consideration for publication elsewhere. If a paper has already
been presented at a conference, it should contain at least 30% new material
before being submitted to this issue. Authors must provide any previously
published material relevant to their submission and describe the additions made.
For questions and further information, please contact
Guest editors:
Joost-Pieter Katoen
RWTH Aachen University (DE) and University of Twente (NL)
Tom Rainforth
University of Oxford (UK)
Hongseok Yang
Korea Advanced Institute of Science & Technology (KAIST, KR)