Full papers
Original research contributions with substantial technical depth and experimental results.
Machine Learning/CORE Unranked
GFM @ ICML 2026
Workshop on Graph Foundation Models: A New Era for Graph Machine Learning
GFM@ICML 2026 is a workshop affiliated with ICML 2026 focused on geometric and topological methods in machine learning, bringing together researchers to explore the intersection of geometry, topology, and machine learning research.
Key deadlines
Verified September 9, 2026Full paper
May 9, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
May 9, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Preliminary results, novel ideas, or position papers with concise presentation.
Journal track
Extended versions of previously published work suitable for journal submission.
Research areas in scope
01
Topics of Interest
Geometric deep learningTopological data analysis in machine learningManifold learning and dimensionality reductionSymmetry and invariance in neural networksRiemannian optimizationAlgebraic topology for representation learningGeometric generative modelsTopological regularization and loss functionsApplications of geometry and topology in vision, NLP, and scientific ML
Policies worth checking twice
- Submissions must be anonymized for double-blind review
- Full papers are limited to 8 pages, short papers to 4 pages (excluding references)
- Dual submission to other venues is not allowed during review
- Author lists and affiliations may be updated after acceptance
- Authors must include an AI-use statement in their submissions
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026