ICML 2026 Call for Tutorials

ICML 2026 Call for Tutorials ICML 2026 will be held in Seoul, South Korea , with tutorials taking place on July 6, 2026 . The ICML tutorial program aims to provide high-quality, in-depth presentations on topics of broad interest to the machine learning community, covering both foundational advances and emerging research directions. Mandatory Tutorial Topics Following an open call for community nominations, ICML 2026 will only consider tutorial proposals that directly address one of the following topics : Beyond-Transformer Sequence Models (e.g., state-space models, S4, Mamba, and related architectures) Diffusion Models: Quantitative and Theoretical Understanding Safety, Machine Unlearning, Watermarking, and Fingerprinting Deep Learning and Deep Reinforcement Learning: Theory and Applications LLM Post-Training and Test-Time Training Theorem Proving and Formal Methods with Lean Proposals that do not clearly fall within one of these topics will not be considered. Tutorial Format Tutorials will be in-person and 2.5 hours long, including time for questions. Each tutorial may have up to two presenters . Each presenter will be offered a complementary full conference registration. Tutorials should be accessible to a broad ICML audience while providing sufficient technical depth. Submission Guidelines Tutorial proposals must be two pages and should clearly describe: The tutorial’s scope and learning objectives The specific content to be covered The intended audience and prerequisites The presenter(s) and their affiliation(s) Any planned supplementary materials (e.g., slides, notes, code) Submissions will be handled through this form Important Dates Submission deadline: Friday, February 6, 2026 (AOE) Notification of decisions: Early March 2026 Review Process All proposals will be reviewed by the ICML 2026 Tutorial Chairs. Selection will be based on relevance to the specified topics, clarity of presentation, expected impact on the community, and balance across topics and presenters. Successful Page Load ICML uses cookies for essential functions only. We do not sell your personal information. Our Privacy Policy » Accept