ICML 2022 Call for Papers The 39th International Conference on Machine Learning (ICML 2022) will be held in Baltimore, Maryland USA July 17-23, 2022 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. There are three important changes in the reviewing, paper formatting and submission process compared to last year: (i) Reviewing will take place in two phases. (ii) Papers need to be prepared and submitted as a single file: 8 pages as main paper, with unlimited pages for references and appendix. (iii) There will be no separate deadline for the submission of supplementary material. In addition, a new requirement is that upon the acceptance of their papers, at least one of the authors must join the conference, either in person or virtually, or their paper will not be included in the proceedings. Important dates: As noted above, this year, ICML will use a two-phase continue reviewing process, with a single review cycle, as follows. Author registration opens Dec 31, 2021. Submissions open Jan 10, 2022. Abstract submission deadline Jan 20, 2022 AOE. Full paper submission deadline Jan 27, 2022 AOE. All paper submission deadlines are "Anywhere On Earth." You may subscribe to these dates in your calendar on the dates <https://icml.cc/Conferences/2022/Dates> page. Abstracts and papers can be submitted through CMT: [link available soon] 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.) Papers published at ICML are indexed in the Proceedings of Machine Learning Research <https://proceedings.mlr.press/> through the Journal of Machine Learning Research. PoliciesDeadlines: 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 abstract by the abstract submission deadline. Submission titles should not be modified after the abstract submission deadline, and abstracts should not be modified by more than 50% after the abstract submission deadline. Submissions violating these rules may be deleted after the paper submission deadline without reviewing. The author list at the 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 abstract 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. Reviewing Criteria: Accepted papers must be based on original research and must contain significant 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, such as sharing it 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. Details of the guideline will be published on the website. Style and Author Instructions: It will be available on the ICML website shortly. Program Chairs: Csaba Szepesvari (Deepmind) Le Song (Mohamed bin Zayed University of AI) Stefanie Jegelka (MIT) General Chair: Kamalika Chaudhuri (UCSD and Facebook AI Research)
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