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Conference on Parsimony and Learning (CPAL)
March 2025, Stanford

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

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 (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.

Stay tuned for further updates!

Key Dates and Deadlines

For a complete list of deadlines, see the deadlines page.

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


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