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: Calls for Papers, Tutorials, Rising Stars Award
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 are now open (OpenReview-based). Please see the call for papers for details about the submission and reviewing process, as well as subject areas of interest and general policies.
This year, CPAL is soliciting tutorial proposals from the community, to be delivered as part of the conference program. Please see the call for tutorials for details and deadlines.
The CPAL Rising Stars Award is now accepting applications from outstanding early-career researchers. Please see the application page for further details and deadlines.
Key Dates and Deadlines
The Proceedings Track submission deadline has been extended to December 2nd, 2024. 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 3rd-6th, 2025: Rebuttal for Submissions to Proceedings Track
- Jan 4th, 2025: Tutorial Proposal Decisions Released
- Jan 5th, 2025: Submission Deadline for Spotlight Track (non-archival)
- January 30th, 2025: Decisions Released (both tracks)
- March 24th–27th, 2025: Conference in-person, Stanford, CA
Keynote Speakers
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
