Call for Papers

Full details, including style files, will be published here in due course.

ProbNum25 will have a dedicated volume in the Proceedings of Machine Learning Research (PMLR), where accepted papers will be published. A submission should be up to 8 pages (or shorter) using dedicated style files (appendix excluded). The submission deadline is scheduled for the 5th of March 2025. Subject areas are methods, theory and applications of probabilistic numerics. The authors of accepted papers will present talks and/or posters at ProbNum25. Details are as follows.

Key Dates

The current schedule, subject to revision, for submission and the review process is as follows.

• Submission Deadline: 5th March 2025.
• Reviews Released: 9th April 2025.
• Responses Due: 18th April 2025.
• Decisions: 14th May 2025.

Subject Areas

ProbNum25 welcomes submissions on methods, theory and applications in Probabilistic Numerics or broader fields involving probabilistic quantification of estimation errors of deterministic quantities (computational uncertainties). Examples of topics are as follows.

Methods (Algorithms)

• Probabilistic (Bayesian or non-Bayesian) numerical methods.
  • e.g., Bayesian quadrature, Bayesian optimisation, probabilistic solvers of ODEs or PDEs, probabilistic numerical linear algebra, especially for Gaussian process regression
• Black-box probabilistic numerics.
• Reproducing kernel-based methods for numerical analysis (corresponding to probabilistic numerical methods)
  • e.g., interpolation, quadrature, maximum mean discrepancy, global optimisation, differential equation solvers, physics-informed learning

Theory

• Error bounds and convergence rates of probabilistic numerical methods.
• Well-calibratedness of uncertainty estimates.
• Properties of hypothesis spaces (e.g., Gaussian processes, reproducing kernel Hilbert spaces) of numerical methods.

Applications

• Computation-aware uncertainty quantification for
  • simulation, including the solution of (partial, ordinary, stochastic, and differential algebraic) differential equations for time-evolving processes.
  • deep learning-based simulation methods, superresolution methods, diffusion models, neural operators, and other algorithms aiming to functionally replace numerical computation.
• Computation-aware optimal planning and decision-making under uncertainty, including for control, robotics, active learning, etc.
• Inverse problems and data assimilation, e.g. in scientific simulation.
• Applications in science and engineering, including (but not limited to) material science, physics and astronomy, climate science, geoscience, and finance.

Submission Instructions

Page limit
Submissions are full papers limited to up 8 pages excluding references, acknowledgements, and appendices. Shorter submissions are very welcome and will be equally considered.

Formatting instructions
Submissions must use the ProbNum LaTeX style package, which will be provided here on this page by 1 January 2025 (at the latest). Please do not modify the style file. Formatting instructions are available in the sample paper provided with the style package.

Anonymization
The ProbNum25 review process is double-blind. All submissions must be anonymized and may not contain any information that can violate the double-blind reviewing policy, such as the author names or their affiliations, acknowledgements, or links that can infer any author’s identity or institution. Self-citations are allowed as long as anonymity is preserved.

Submission page
The submission will be done via OpenReview. The submission page will be available by early 2025.
Please upload a single file; you can either submit a single pdf file or a single zip file for further supplementary material in other formats.

Dual submissions
Submitted manuscripts should not have been previously published in a journal or in the proceedings of a conference, and should not be under consideration for publication at another conference at any point during the ProbNum25 review process. This excludes non-archival venues such as workshops.

Confidentiality
Reviewer will be instructed to keep them confidential during the review process and delete them once the review process has concluded.

Reviewer nomination

For each submission, the authors will be expected to nominate at least one of the authors as a reviewer for ProbNum25. Nominated reviewers are expected to have sufficient expertise in the relevant field.