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ICML 2023 : International Conference on Machine Learning


Conference Series : International Conference on Machine Learning
When Jul 24, 2023 - Jul 30, 2023
Where Seoul, South Korea
Submission Deadline TBD

Call For Papers

ICML 2023 Call For Papers

The 40th International Conference on Machine Learning (ICML 2022) will be held in Honolulu, Hawaii USA July 23rd - July 29th, 2023, and is planned to be an in person conference with virtual elements. In addition to the main conference sessions, the conference will also include Expo, Tutorials, and Workshops. Please submit proposals to the appropriate chairs.

We invite submissions of papers on all topics related to machine learning for the main conference proceedings. All papers will be reviewed in a double-blind process and accepted papers will be presented at the conference. As with last year, papers need to be prepared and submitted as a single file: 8 pages as main paper, with unlimited pages for references and appendix. There will be no separate deadline for the submission of supplementary material. In addition, we require that, barring exceptional circumstances (such as visa problems) upon the acceptance of their papers, at least one of the authors must attend the conference, in person.

Important dates:

As noted above, this year, ICML will use a single paper submission deadline with a single review cycle, as follows.

Submissions open Jan 9th, 2023.

Full paper submission deadline Jan 26th, 2023 3pm EST.

Abstracts and papers can be submitted through OpenReview:

Topics of interest include (but are not limited to):

General Machine Learning (active learning, clustering, online learning, ranking, reinforcement learning, supervised, semi- and self-supervised learning, time series analysis, etc.)
Deep Learning (architectures, generative models, deep reinforcement learning, etc.)
Learning Theory (bandits, game theory, statistical learning theory, etc.)
Optimization (convex and non-convex optimization, matrix/tensor methods, stochastic, online, non-smooth, composite, etc.)
Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness, etc.)
Applications (computational biology, crowdsourcing, healthcare, neuroscience, social good, climate science, etc.)

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