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.
Announcing CPAL 2025
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 Recent Spotlight Track of the second Conference on Parsimony and Learning remain open (OpenReview-based). Please see the call for papers for details about deadlines, submission, and the reviewing process, as well as subject areas of interest and general policies.
Key Dates and Deadlines
The Spotlight Track submission deadline has been extended to January 12th, 2025. For a complete list of deadlines, see the deadlines page.
- Dec 2nd, 2024: Submission Deadline for Proceedings Track (archival)
- Dec 6th, 2024: Application Deadline for Tutorial Proposals
- Dec 15th, 2024: Application Deadline for Rising Stars Award
- Jan 12th, 2025: Submission Deadline for Spotlight Track (non-archival)
- Jan 18th–24th, 2025: Rebuttal Period for Submissions to Proceedings Track
- Jan 21st, 2025: Tutorial Proposal and Rising Stars Award Decisions Released
- Feb 7th, 2025: Paper Decisions Released (both tracks)
- Mar 24th–27th, 2025: Conference in-person, Stanford, CA
Keynote Speakers
Richard Baraniuk
Rice University
Alison Gopnik
University of California, Berkeley
Fred Kjolstad
Stanford University
Konrad Kording
University of Pennsylvania
Jason Lee
Princeton University
Andrea Montanari
Stanford University
Yuandong Tian
Meta AI Research
Doris Tsao
University of California, Berkeley
Michael Unser
École Polytechnique Fédérale de Lausanne (EPFL)
Sponsors
