AI/CORE A*

ICML 2026

International Conference on Machine Learning

July 6-12, 2026Seoul, KoreaOfficial conference site Site reachable

ICML 2026 is the Forty-Third International Conference on Machine Learning, held in Seoul, South Korea, from July 6–11, 2026. It is a premier venue for presenting and publishing cutting-edge research in machine learning and its applications in AI, statistics, data science, and domains like healthcare, robotics, and climate science, attracting a global community of researchers, practitioners, and students.

Official CFP Back to deadlines Verified September 9, 2026

Key deadlines

Verified September 9, 2026

Abstract registration

January 24, 2026

AoE

Full paper

January 29, 2026

AoE

Conference timeline

Submission and decisions

Abstract registrationKey deadline

January 24, 2026 · AoE

Full paperKey deadline

January 29, 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 of significant interest to the machine learning community, submitted as an 8-page main paper with unlimited pages for references, impact statement, and appendices. Accepted papers may be presented in person or included in proceedings without presentation.

Position Papers

Submissions invited separately from the main track, focusing on provocative ideas, open problems, or visionary perspectives in machine learning.

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 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.)

Policies worth checking twice

Submissions must be double-blind and anonymized; no reference to non-anonymized versions during review.
Authors may post preprints (e.g., on arXiv) but must not advertise work as an ICML submission during the review period.
Dual or concurrent submission to other conferences or journals is prohibited; substantial overlap with prior work leads to rejection.
Papers must be submitted as a single file: 8 pages for main text, unlimited for references, impact statement, and appendices; accepted papers receive one extra page.
Abstract and paper deadlines are strict with no extensions allowed.
Author list cannot be changed after the abstract submission deadline; additions/removals require case-by-case approval by program chairs.
Each submission must designate at least one qualified author as a reciprocal reviewer, with a maximum of two submissions per reviewer.
Authors with four or more submissions must agree to serve as a reviewer (threshold may be lowered to three if needed).

Official sources

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

Official conference site Call for Papers Call for Tutorial Nominations Call for Workshops Call for Tutorials Program Committee

Last verified September 9, 2026