The Conference on Parsimony and Learning (CPAL) is an annual research conference focused on addressing the parsimonious, low dimensional structures that prevail in machine learning, signal processing, optimization, and beyond. We are interested in theories, algorithms, applications, hardware and systems, as well as scientific foundations for learning with parsimony.
Call for Papers
We are pleased to announce the Second Conference on Parsimony and Learning, to be held in concert with Stanford Data Science at Stanford University in California, USA!
Paper submissions for the second Conference on Parsimony and Learning will be opened soon. Please see the call for papers for details about the submission and reviewing process, as well as subject areas of interest and general policies.
Stay tuned for further updates!
Key Dates and Deadlines (Tentative)
All deadlines are 23:59 Anywhere-on-Earth (AOE)
- 3rd Week of November, 2024: Submission Deadline
- 1st Week of Jan, 2025: Rebuttal
- January 31st, 2025: Decisions Released
- Week of March 24th 2025: Conference in-person, Stanford, CA
