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.
Register for CPAL: March 24th–27th, 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!
All CPAL attendees are required to register. See the registration page for details about available tickets and costs.
CPAL 2025 Program Announced!
The CPAL 2025 review process is complete – congratulations to all authors of accepted papers!
The tentative program has been announced, and can be found on the schedule page.
Accepted papers for the Proceedings Track and Recent Spotlight Track have been released. Information about poster presentation sessions at CPAL can be found here. Information about oral sessions for for Proceedings Track papers accepted as oral presentations can be found here.
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
Yingyu Liang
University of Hong Kong, University of Wisconsin-Madison
Yuandong Tian
Meta AI Research
Doris Tsao
University of California, Berkeley
Michael Unser
École Polytechnique Fédérale de Lausanne (EPFL)
Sponsors
Conference Sponsors
Conference Host
Platinum Sponsor
