ICML: International Conference on Machine Learning

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2025 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
ICML 2024 International Conference on Machine Learning
Jul 21, 2024 - Jul 27, 2024 Vienna, Austria Feb 1, 2024
ICML 2023 International Conference on Machine Learning
Jul 24, 2023 - Jul 30, 2023 Seoul, South Korea TBD
ICML 2022 39th International Conference on Machine Learning
Jul 17, 2022 - Jul 23, 2022 Baltimore, United States Feb 3, 2022 (Jan 27, 2022)
ICML 2021 International Conference on Machine Learning
Jul 18, 2021 - Jul 24, 2021 Virtual Feb 4, 2021 (Jan 28, 2021)
ICML 2020 37th International Conference on Machine Learning
Jul 12, 2020 - Jul 18, 2020 Vienna, AUSTRIA Feb 6, 2020 (Jan 30, 2020)
ICML 2019 36th International Conference on Machine Learning
Jun 10, 2019 - Jun 15, 2019 Long Beach, CA, USA Jan 23, 2019 (Jan 18, 2019)
ICML 2018 The 35th International Conference on Machine Learning
Jul 10, 2018 - Jul 15, 2018 Stockholmsmässan, Stockholm SWEDEN Feb 9, 2018
ICML 2017 34th International Conference on Machine Learning
Aug 6, 2017 - Aug 11, 2017 Sydney, Australia Feb 24, 2017
ICML 2015 International Conference on Machine Learning 2015
Jul 6, 2015 - Jul 11, 2015 Lille, France Feb 6, 2015
ICML 2010 The 27th International Conference on Machine Learning
Jun 21, 2010 - Jun 24, 2010 Haifa, Israel Feb 1, 2010
ICML 2009 The 26th International Conference On Machine Learning
Jun 14, 2009 - Jun 18, 2009 Montreal, Canada Jan 26, 2009
ICML 2008 International Conference on Machine Learning
Jul 6, 2008 - Jul 9, 2008 Helsinki, Finland Feb 8, 2008
 
 

Present CFP : 2024

ICML 2024 Call For Papers

The 41st International Conference on Machine Learning (ICML 2024) will be held in Vienna, Austria, July 21st - 27th, 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, 2024.

Full paper submission deadline February 1st, 2024 AoE (see https://time.is/Anywhere_on_Earth ).

Abstracts and papers can be submitted through OpenReview: https://openreview.net/group?id=ICML.cc/2024/Conference

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.) - we do welcome strong application papers even if there is no new method or theory

This year we are introducing a separate track for position papers. Please consider submitting to this track if you feel that your work would be a better fit for this track instead of the main conference track. We anticipate similar acceptance rates for this track and the main conference track.

Papers published at ICML are indexed in the Proceedings of Machine Learning Research through the Journal of Machine Learning Research.


Policies

Deadlines:

Abstract and paper submission deadlines are strict. In no circumstances will extensions be given.

Changes of title/abstract/authorship:

Authors should include a full title for their paper, as well as a complete paper by the paper submission deadline. Submission titles should not be modified after the paper submission deadline. Submissions violating these rules may be deleted after the paper submission deadline without reviewing. The author list at the paper submission deadline will be considered final, and no changes in authorship will be permitted for accepted papers.

Double-Blind Review:

All submissions must be anonymized and may not contain any information with the intention or consequence of violating the double-blind reviewing policy, including (but not limited to) citing previous works of the authors or sharing links in a way that can infer any author’s identity or institution, actions that reveal the identities of the authors to potential reviewers.

Authors are allowed to post versions of their work on preprint servers such as arXiv. They are also allowed to give talks to restricted audiences on the work(s) submitted to ICML during the review. If you have posted or plan to post a non-anonymized version of your paper online before the ICML decisions are made, the submitted version must not refer to the non-anonymized version.

ICML strongly discourages advertising the preprint on social media or in the press while under submission to ICML. Under no circumstances should your work be explicitly identified as ICML submission at any time during the review period, i.e., from the time you submit the paper to the communication of the accept/reject decisions.

Dual Submission:

It is not appropriate to submit papers that are identical (or substantially similar) to versions that have been previously published, accepted for publication, or submitted in parallel to other conferences or journals. Such submissions violate our dual submission policy, and the organizers have the right to reject such submissions, or to remove them from the proceedings. Note that submissions that have been or are being presented at workshops do not violate the dual-submission policy, as long as there’s no associated archival publication.

Reviewing Criteria:

Accepted papers must be based on original research and must contain novel results of significant interest to the machine learning community. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact. Reproducibility of results and easy availability of code will be taken into account in the decision-making process whenever appropriate.

Ethics:

Authors and members of the program committee, including reviewers, are expected to follow standard ethical guidelines. Plagiarism in any form is strictly forbidden as is unethical use of privileged information by reviewers, ACs, and SACs, such as sharing this information or using it for any other purpose than the reviewing process. All suspected unethical behaviors will be investigated by an ethics board and individuals found violating the rules may face sanctions. This year, we will collect names of individuals that have been found to have violated these standards; if individuals representing conferences, journals, or other organizations request this list for decision making purposes, we may make this information available to them.

The use of LLMs is allowed as a general-purpose writing assist tool. Authors should understand that they take full responsibility for the contents of their papers, including content generated by LLMs that could be construed as plagiarism or scientific misconduct (e.g., fabrication of facts). LLMs are not eligible for authorship.

Impact Statements:

Authors are required to include a statement of the potential broader impact of their work, including its ethical aspects and future societal consequences. This statement should be in a separate section at the end of the paper (co-located with Acknowledgements, before References), and does not count toward the paper page limit. In many cases, where the ethical impacts and expected societal implications are those that are well established when advancing the field of Machine Learning, substantial discussion is not required, and a simple statement such as:

“This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.”

The above statement can be used verbatim in such cases, but we encourage authors to think about whether there is content which does warrant further discussion, as this statement will be apparent if the paper is later flagged for ethics review.

Financial aid:

Each paper submission should, by providing a corresponding icml.cc account email address, designate up to one student author who, should the paper be accepted, would benefit substantially from a grant to present at the conference. Doing so confirms (1) financial need, (2) intention to attend and present in person and (3) willingness to volunteer at the conference for two 4 hour shifts. ICML aims to provide free conference registration and hotel for at least part of the week, though may provide smaller awards. The number of awards is limited and only designated student authors will be considered for financial aid

OpenReview and Rankings:

This year we will use OpenReview and we will require that authors of multiple submissions, upon submission confirmation, submit a rank ordering of their papers from their own perspective. For this year, we seek this information to assess consistency of self-perception with respect to review outcomes. We will not share rankings with co-authors, reviewers, ACs, or SACs. Rankings will not be used in decision-making processes.

Author Instructions, Style Files and an Example Paper. Submitted papers that do not conform to these policies will be rejected without review. Authors are kindly asked to make their submissions as accessible as possible for everyone including people with disabilities and sensory or neurological differences.
 

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