Overview
The 3rd Conference on Parsimony and Learning (CPAL 2026) is set to take place from March 23 to 26, 2026, in Tübingen, Germany. This event, hosted by the Max Planck Institute for Intelligent Systems and the ELLIS Institute Tübingen, aims to gather researchers from various fields to discuss the significance of parsimony in machine learning and related domains.
Background & Relevance
Parsimony in machine learning refers to the principle of simplicity in models and algorithms, emphasizing low-dimensional structures. This concept is crucial across various disciplines, including signal processing, optimization, and applied mathematics. The CPAL conference serves as a platform for researchers to explore theories, algorithms, and applications that align with this principle, fostering collaboration and innovation in understanding intelligence and scientific methodologies.
Key Details
- Conference Dates: March 23–26, 2026
- Location: Tübingen, Germany
- Submission Tracks:
- Proceedings Track (archival): Double-blind review, 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.
- Important Dates:
- December 5, 2025: Proceedings submission deadline
- December 10, 2025: Tutorial proposal deadline
- December 15, 2025: Rising Stars application deadline
- January 8–11, 2026: Rebuttal period
- January 14, 2026: Tutorial results announced
- January 15, 2026: Recent Spotlight submission deadline
- January 20, 2026: Final decisions released
Eligibility & Participation
The conference invites contributions from researchers, practitioners, and students interested in the fields of machine learning, optimization, signal processing, and applied mathematics. It targets those who are exploring the implications of parsimony in their work and looking to share insights with a broader audience.
Submission or Application Guidelines
To participate, authors should prepare their submissions according to the specified tracks. The proceedings track requires a double-blind review process, while the spotlight track allows for single-blind reviews of recent work. Authors should adhere to the page limits and formatting guidelines as outlined on the conference website.
Additional Context / Real-World Relevance
The exploration of parsimonious learning has significant implications in various domains, including engineering, medicine, and social sciences. By focusing on efficient learning methods, researchers can contribute to advancements that are not only theoretically sound but also practically applicable in real-world scenarios.
Conclusion
CPAL 2026 presents an excellent opportunity for researchers to engage with cutting-edge topics in parsimony and learning. Interested individuals are encouraged to submit their work, participate in discussions, and connect with peers in the field. For more information, visit the CPAL website.
Category: CFP & Deadlines
Tags: machine learning, optimization, signal processing, theory, artificial intelligence, parsimony, data science, CPAL, Max Planck Institute, ELLIS Institute, Tübingen
