Title:
The International Conference on Machine Learning
Description:
The 42nd International Conference on Machine Learning (ICML 2025) will be held in Vancouver, Canada, July 13–19, and is planned to be an in-person conference. 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. We require that, barring exceptional circumstances, at least one of the authors of accepted papers attend the conference in person to present the paper.
See information on Author Instructions, Style Files, and an Example Paper. 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 (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.)