AISTATS 2024 : 27th International Conference on Artificial Intelligence and Statistics

We invite submissions to the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), and welcome paper submissions on artificial intelligence, machine learning, statistics, and related areas.

AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. The conference is committed to diversity in all its forms, and encourages submissions from authors of underrepresented groups and geographies in ML/AI.

Key dates
Abstract deadline: 6 October 2023 (Anywhere on Earth)
Paper submission deadline: 13 October 2023 (Anywhere on Earth)
Appendix submission deadline: 20 October 2023
Reviews released: 27 November 2024
Author rebuttals due: 5 December 2024 (Anywhere on Earth)
Paper decision notifications: 19 January 2024
Conference dates: May 2 - May 4, 2024



Paper Submission (Proceedings Track)
The proceedings track is the standard AISTATS paper submission track. Papers will be selected via a rigorous double-blind peer-review process. All accepted papers will be presented at the Conference as contributed talks or as posters and will be published in the Proceedings.

Solicited topics include, but are not limited to:

Machine learning methods and algorithms (classification, regression, unsupervised and semi-supervised learning, clustering, logic programming, …)
Probabilistic methods (Bayesian methods, approximate inference, density estimation, tractable probabilistic models, probabilistic programming, …)
Theory of machine learning and statistics (optimization, computational learning theory, decision theory and bandits, game theory, frequentist statistics, information theory, …)
Deep learning (theory, architectures, reinforcement learning, generative models, optimization for neural networks, …)
Ethical and trustworthy machine learning (causality, fairness, interpretability, privacy, robustness, safety, …)
Applications of machine learning and statistics (including natural language, signal processing, computer vision, physical sciences, social sciences, sustainability and climate, healthcare, …)
Formatting and Supplementary Material
Submissions are limited to 8 pages excluding references using the LaTeX style file we provide below (the page limit will be 9 for camera-ready submissions). The number of pages containing only citations and the reproducibility checklist is not limited. You can also submit a single file of additional supplementary material which may be either a pdf file (such as proof details) or a zip file for other formats/more files (such as code or videos). Note that reviewers are under no obligation to examine the supplementary material. If you have only one supplementary pdf file, please upload it as is; otherwise gather everything to the single zip file.

Submissions are accepted at https://cmt3.research.microsoft.com/AISTATS2024/.

Formatting information (including LaTeX style files) is available in the AISTATS2024PaperPack. We do not support submission in preparation systems other than LaTeX. Please do not modify the layout given by the style file. If you have questions about the style file or its usage, please contact the publications chair or the program chairs via [email protected].

Reviewer Nomination
For each submission, the authors will be expected to nominate at least one of the authors as a reviewer for AISTATS 2024. Nominated reviewers are expected to have sufficient expertise in the relevant field. Kindly understand that by a recent increase of submissions, we need more reviewers than previous years.

Anonymization Requirements
The AISTATS review process is double-blind. All submissions must be anonymized and may not contain any information that can violate the double-blind reviewing policy, such as the author names or their affiliations, acknowledgements, or links that can infer any author’s identity or institution. Self-citations are allowed as long as anonymity is preserved. It is up to the author’s discretion how best to preserve anonymity when including self-citations. Possibilities include: leaving out a self-citation, including it but replacing the citation text with “removed for anonymous submission,” or leaving the citation as-is. We recommend leaving in a moderate number of self-citations for published or otherwise well-known work.

We suggest the authors refrain from advertising the preprint on social media or in the press while under submission to AISTATS. Preprints must not be explicitly identified as an AISTATS submission at any time during the review period (i.e., from the abstract submission deadline until the communication of the accept/reject decision).

Dual Submissions
Submitted manuscripts should not have been previously published in a journal or in the proceedings of a conference, and should not be under consideration for publication at another conference at any point during the AISTATS review process. Submissions as extended abstracts (4 pages or less), to workshops or non-archival venues (without a proceedings), or to arXiv, will not be considered a concurrent submission. It is acceptable to have a substantially extended version of the submitted paper under consideration simultaneously for journal publication, so long as the journal version’s planned publication date is in May 2024 or later and the journal submission does not interfere with AISTATS’ right to publish the paper. Authors are also allowed to give talks on the work(s) submitted to AISTATS during the review, but these talks should not identify papers as AISTATS submissions.

Reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.

All accepted papers will be presented at the Conference either as contributed talks or as posters, and will be published in the AISTATS Conference Proceedings in the Journal of Machine Learning Research Workshop and Conference Proceedings series. Papers for talks and posters will be treated equally in publication.

Confidentiality
The reviewers and area-chairs of your paper will have access to your paper and supplementary material. In addition, the program chairs and workflow chairs will have access to all the papers. Everyone having access to papers and supplementary materials will be instructed to keep them confidential during the review process and delete them after the final decisions.

Reviews will be visible to area chairs, program chairs, and workflow chairs throughout the process. Reviewers will get access to other reviews for a paper after they have submitted their own review.

Author names will be visible to program chairs and workflow chairs. Reviewers and area chairs will not know the author names at any stage of the process. Reviewer names are visible to the area chairs, workflow chairs, and program chairs.