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.
Full Agenda
Tickets
| Registration Type | What's included | Price |
|---|
| General Admission/Industry | Access to all academic sessions, coffee breaks | $425 |
| Student Pass (60% off) | Access to all academic sessions, coffee breaks | $170 |
| Tutorials–General Public | | $275 |
| Totorials–Students (60% off) | | $110 |
Keynote Speakers
Alison Gopnik, University of California, Berkeley
Doris Tsao, University of California, Berkeley
Fred Kjolstad, Stanford University
Jason Lee, Princeton University
Konrad Kording, University of Pennsylvania
Michael Unser, École Polytechnique Fédérale de Lausanne (EPFL)
Richard Baraniuk, Rice University
Yingyu Liang, University of Hong Kong, University of Wisconsin-Madison
Yuandong Tian, Meta AI Research
Visit the CPAL website for key dates, confirmed speakers, and more information.
