Machine Learning/CORE Unranked

ICML 2026

Forty-third International Conference on Machine Learning (ICML) - Workshop Proposals (2026)

Jul 07 2026Seoul, South KoreaOfficial workshop site Site reachable

ICML 2026 is the 43rd International Conference on Machine Learning, held in Seoul, South Korea, from July 6–11, 2026. It features a main conference with peer-reviewed papers, tutorials, workshops, and an expo, focusing on original and rigorous research across all areas of machine learning, with an emphasis on inclusivity, ethical considerations, and community engagement.

Official CFP Back to deadlines Verified September 9, 2026

Key deadlines

Verified September 9, 2026

Full paper

February 14, 2026

AoE

Workshop timeline

Submission and decisions

Full paperKey deadline

February 14, 2026 · AoE

Paper fit

Contribution paths

A strong submission should clearly identify its contribution and evaluate it appropriately.

Main Conference Papers

Original and rigorous research papers up to 8 pages for the main text, with unlimited pages for references, impact statements, and appendices. Accepted papers may be presented in person or included in proceedings without presentation.

Position Papers

Separately submitted papers that present provocative ideas, open problems, or community perspectives, handled independently from main track submissions.

Camera-Ready Papers with Lay Summaries

Accepted papers must submit a camera-ready version and a short 'lay summary' (plain language summary) for public communication.

Research areas in scope

Topics of Interest

general machine learning (active learning, clustering, online learning, ranking, supervised, semi- and self-supervised learning, time series analysis, etc.)deep learning (architectures, generative models, theory, etc.)evaluation (methodology, meta studies, replicability and validity, human-in-the-loop, etc.)theory of machine learning (statistical learning theory, bandits, game theory, decision theory, etc.)machine learning systems (improved implementation and scalability, hardware, libraries, distributed methods, etc.)optimization (convex and non-convex optimization, matrix/tensor methods, stochastic, online, non-smooth, composite, etc.)probabilistic methods (Bayesian methods, graphical models, Monte Carlo methods, etc.)reinforcement learning (decision and control, planning, hierarchical RL, robotics, etc.)trustworthy machine learning (reliability, causality, fairness, interpretability, privacy, robustness, safety, etc.)application-driven machine learning (innovative techniques, problems, and datasets driven by healthcare, physical sciences, biosciences, social sciences, sustainability, and climate, etc.)

Policies worth checking twice

Submissions must be double-blind and anonymized; no references to non-anonymized preprints during review.
Dual or concurrent submission to other conferences or journals is prohibited; submissions must be original and not substantially similar to prior or concurrent work.
Authors must designate at least one qualified reciprocal reviewer per submission, with a maximum of two submissions per author designated as reciprocal reviewer.
Authors with four or more submissions must agree to serve as a reviewer for ICML (threshold may be lowered to three if needed).
Authors may not change the author list after the abstract deadline; additions or removals require written justification and approval by program chairs.
All papers must include an impact statement addressing ethical and societal consequences, which does not count toward the page limit.
Generative AI tools (e.g., LLMs) may be used for assistance but cannot be authors; prompt injection is strictly forbidden and leads to desk rejection.
Authors must take full responsibility for all content, including AI-generated material, and disclose notable AI use in the methodology.

Official sources

Compiled from the official call for papers. The organizers’ pages remain authoritative.

Official workshop site Call for Papers Call for Tutorial Nominations Call for Tutorials Program Committee Author Instructions

Last verified September 9, 2026