Full papers
Original research contributions with comprehensive analysis and evaluation.
Machine Learning/CORE Unranked
CTB@ICML 2026
ICML 2026 Workshop on Combining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model Performance
The CTB@ICML 2026 workshop focuses on collaborative and transparent machine learning, bringing together researchers to explore methods that enhance reproducibility, interpretability, and ethical accountability in AI systems. It serves as a forum for presenting cutting-edge work at the intersection of machine learning and societal impact.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Concise presentations of novel ideas, preliminary results, or position statements.
Demo submissions
Interactive demonstrations of systems or tools related to collaborative and transparent AI.
Research areas in scope
01
Workshop Topics
Collaborative machine learningTransparency and interpretability in AIReproducibility and benchmarkingEthical AI and fairnessHuman-AI collaborationExplainable decision-makingOpen science in machine learningAI for social good
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 conferences or journals is not permitted.
- Author lists cannot be changed after the submission deadline.
- All submissions must include a statement on the use of AI tools in the research process.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026