CPAL 2026 - Call for papers

[Shiwei Liu]

Dear all,

We are excited to announce the 3rd Conference on Parsimony and Learning (CPAL 2026)! The conference will take place March 23–26, 2026, in Tübingen, Germany, hosted by the Max Planck Institute for Intelligent Systems and the ELLIS Institute Tübingen.

About CPAL

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.  We envision the conference as a general scientific forum where researchers in machine learning, applied mathematics, signal processing, optimization, intelligent systems, and all associated science and engineering fields can gather, share insights, and ultimately work towards a common modern theoretical and computational framework for understanding intelligence and science from the perspective of parsimonious learning.

For details, please see https://cpal.cc

Submission Tracks

• Proceedings Track (archival): Double-blind review, hosted on OpenReview. Up to 9 pages (excluding references/appendix).
• Recent Spotlight Track (non-archival): Single-blind review. Accepts under-review or recently accepted work. Up to 9 pages + abstract, with supplemental materials allowed.

Important Dates

• Dec 5, 2025: Proceedings submission deadline
• Dec 10, 2025: Tutorial proposal deadline
• Dec 15, 2025: Rising Stars application deadline
• Jan 8–11, 2026: Rebuttal period
• Jan 14, 2026: Tutorial results announced
• Jan 15, 2026: Recent Spotlight submission deadline
• Jan 20, 2026: Final decisions released
• Mar 23–26, 2026: Conference in Tübingen, Germany

Topics of Interest

CPAL welcomes contributions in (but not limited to):

• Theory & Foundations: Sparse coding, structured sparsity, low-dimensional manifolds, dictionary learning, connections to deep learning theory, equivariance/invariance, theoretical neuroscience foundations.
• Optimization & Algorithms: Robust and generalizable learning, efficient and interpretable architectures, data- and compute-efficient training, unrolled optimization, adaptive inference.
• Data, Systems & Applications: Datasets and benchmarks, inverse problems, hardware and co-design, parsimonious learning in intelligent systems, applications across science, engineering, medicine, and social sciences.

Confirmed Keynote Speakers

• Bernhard Schölkopf (MPI for Intelligent Systems)
• Francis Bach (INRIA / ENS)
• Niao He (ETH Zurich)
• Taiji Suzuki (University of Tokyo / RIKEN AIP)
• Fanny Yang (ETH Zurich)
• Andreas Krause (ETH Zurich)
• More are confirming

Stay Connected

• Website: https://cpal.cc
• Twitter/X: https://x.com/CPALconf
• LinkedIn: https://www.linkedin.com/company/conference-on-parsimony-and-learning-cpal/
• Bluesky: https://bsky.app/profile/cpalconf.bsky.social

Best wishes,

CPAL 2026 Organization Community

https://cpal.cc/organization_committee/