Title:
The International Conference on Machine Learning
Description:
The 43rd International Conference on Machine Learning (ICML 2026) will be held in Seoul, South Korea, July 6-11, as an in-person event. In addition to the main conference sessions, the conference will include tutorials, workshops, and an expo.
We invite submissions of papers on original and rigorous research of significant interest to the machine learning community for the main conference proceedings. All papers will be reviewed in a double-blind process and accepted papers will be presented at the conference. Papers must be prepared and submitted as a single file: 8 pages for the main paper, with unlimited pages for references, the impact statement, and appendices. There will be no separate deadline for the submission of supplementary material. The final versions of accepted papers will be allowed one extra page for the main paper.
Change in policy: Attendance for authors of accepted papers is optional. After acceptance notifications, the authors will be able to decide by a specified date whether they wish to present their paper in person at the conference or they just wish to include their paper in the proceedings (without presentation at the conference). Regardless of this choice, all the accepted papers will receive equivalent treatment in the proceedings. They will all be eligible for ICML awards as well as for the designations of distinction corresponding to the past “oral presentations” and “spotlight posters.” For proceedings-only papers, at least one of the authors must obtain virtual registration.
Change in policy: Publication of originally submitted version in addition to camera-ready version for accepted papers. For all accepted papers, we will publish the following material in addition to camera-ready version: the originally submitted version (including supplementary material), anonymized reviews, meta-reviews, rebuttal, and reviewer-author discussion. The authors of rejected submissions will also have an option to have their originally submitted version, reviews, meta-reviews, rebuttal, and reviewer-author discussion published.
Other notable changes this year: We have a new Policy for LLM use in Reviewing and we are imposing a cap on the number of papers that can designate the same person as a reciprocal reviewer.
Please review Author Instructions, an Example Paper, and use the correct Style Files. Submitted papers that do not conform to these policies will be rejected without review.
Topics of interest include (but are not limited to):
- 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 that are of interest to the machine learning community and driven by the needs of end-users in applications such as healthcare, physical sciences, biosciences, social sciences, sustainability, and climate etc.)
Similar to last year, we also invite submissions of position papers. Please review the Call for Position Papers; submissions will be handled separately from the main track submissions.
Papers published at ICML are indexed in the Proceedings of Machine Learning Research through the Journal of Machine Learning Research.