Overview
The 3rd Conference on Parsimony and Learning (CPAL 2026) invites researchers to submit their work, emphasizing the significance of parsimony in machine learning and related domains. Scheduled for March 23–26, 2026, in Tübingen, Germany, this event is organized by the Max Planck Institute for Intelligent Systems and the ELLIS Institute Tübingen. The conference serves as a platform for sharing insights and fostering collaboration among experts in various fields.
Background & Relevance
Parsimony in machine learning refers to the principle of preferring simpler models that can explain data effectively without unnecessary complexity. This concept is crucial across numerous domains, including signal processing, optimization, and applied mathematics. By focusing on low-dimensional structures, researchers can develop more efficient algorithms and systems, ultimately enhancing the understanding of intelligence and scientific principles.
Key Details
- Conference Dates: March 23–26, 2026
- Location: Tübingen, Germany
- Submission Tracks:
- Proceedings Track: Double-blind review, archival, up to 9 pages (excluding references/appendix).
- Recent Spotlight Track: Single-blind review, non-archival, accepts under-review or recently accepted work, up to 9 pages + abstract.
- 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
For further information, visit CPAL Website.
Eligibility & Participation
The conference is open to researchers, practitioners, and students interested in the fields of machine learning, optimization, signal processing, and related areas. Contributions from diverse backgrounds are encouraged to foster interdisciplinary collaboration.
Submission or Application Guidelines
To participate, authors should prepare their submissions according to the specified guidelines for each track. The proceedings track requires double-blind submissions, while the spotlight track allows for single-blind submissions. Authors must adhere to the page limits and formatting requirements outlined on the conference website.
Additional Context / Real-World Relevance
The focus on parsimony in learning has significant implications for various applications, including intelligent systems, data analysis, and optimization problems. By promoting efficient learning methods, researchers can contribute to advancements in technology and science, ultimately benefiting multiple sectors such as healthcare, engineering, and social sciences.
Conclusion
CPAL 2026 presents a valuable opportunity for researchers to share their findings and engage with peers in the field of parsimony and learning. Interested individuals are encouraged to submit their work, participate in discussions, and explore the latest advancements in this vital area of research.
Category: CFP & Deadlines
Tags: machine learning, optimization, signal processing, parsimony, theoretical neuroscience, deep learning, intelligent systems, data-efficient training
