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Call for Papers: AISTATS 2026

We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026) and welcome paper submissions at the intersection of artificial intelligence, machine learning, statistics, and related areas. Accepted papers will be presented at the conference to be held in person in Morocco on May 2–5, 2026. At least one author of each accepted paper should register and present the work at the conference. Exceptions may be granted in case of travel emergencies or visa issues.

AISTATS is an interdisciplinary gathering of researchers from 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 submission deadline: September 25, 2025 AOE
  • Full paper submission deadline: October 2, 2025 AOE
  • Supplementary material deadline: October 9, 2025 AOE

Paper Submission (Proceedings Track)

The proceedings track is the standard AISTATS paper submission track. This year, there will be a separate journal track for papers that have been recently published at select top journals to present those works at AISTATS as a poster; the details of this track will be posted separately.

Papers will be selected for publication via a rigorous double-blind peer-review process. Acceptance rates tend to be around 25%. All accepted papers will be presented at the Conference as posters, with a subset being presented as talks, and will be published in the AISTATS Conference Proceedings, as part of the Journal of Machine Learning Research Workshop and Conference Proceedings series, by the publisher Proceedings of Machine Learning Research (PMLR). Papers for talks and posters are treated equally in publication.

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, online learning and bandits, game theory, frequentist statistics, information theory, …)
  • Deep learning (theory, architectures, generative models, optimization for neural networks, …)
  • Reinforcement learning (theory of RL, offline/online RL, deep RL, multi-agent RL, …)
  • 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, …)

Changes of Title/Abstract/Authorship

The author list at the *abstract* submission deadline will be considered final, and no changes in authorship will be allowed. This is to avoid new unforeseen conflicts of interest after the bidding, which will start immediately after the abstract submission deadline. The author order can be changed after the paper is accepted. Submissions violating these rules may be deleted after the paper submission deadline without review. Major changes to the title and abstract after the abstract deadline will also be flagged for desk rejection after review.

Formatting and Supplementary Material

Submissions are limited to 8 pages (excluding references, the reproducibility checklist, and additional appendices) using the LaTeX style file we provide on this webpage (the page limit will be 9 for camera-ready submissions). The number of pages containing only citations, the reproducibility checklist, and appendices (such as proof details) is not limited. Any other additional supplementary material must be submitted separately in a single PDF file or a ZIP file for other formats/more files (such as code or videos). You can choose to submit the additional details either as part of the main submission (after the references and the reproducibility checklist) or as a separate document in the supplementary material. It is the authors' responsibility that any supplementary material does not conflict in content with the main paper (e.g., the separately uploaded additional material is not an updated version of the one appended to the manuscript).

Formatting information (including LaTeX style files): https://aistats.org/aistats2026/AISTATS2026PaperPack.zip

Submissions are accepted through OpenReview at https://openreview.net/group?id=aistats.org/AISTATS/2026/Conference.

The site will start accepting submissions on September 1, 2025.

Update September 3, 2025: This section has been updated to allow supplementary material to be appended to the end of the main submission PDF (after the references and reproducibility checklist).

Reviewer Nomination

For each submission, the authors must nominate at least one of the authors as a reviewer for AISTATS 2026, who may be assigned up to 3 papers for review (since each submission triggers at least 3 full reviews). Nominated reviewers must have sufficient expertise in the relevant field, for example, multiple publications at the top AI/ML conferences, or multiple top journal papers in statistics or related fields. 

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 (being referred to in third person, like other citations). 

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). If it is being posted to arXiv (or similar sites), we suggest using a neutral (non-AISTATS) format. 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.

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) will not be considered a concurrent submission. Tech reports (on arXiv or similar sites) and short papers in workshops without archival proceedings do not count as prior 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 papers. Everyone having access to papers is 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 during the rebuttal phase.

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.

Use of Generative AI

Authors are allowed to use generative AI tools such as Large Language Models (LLMs) to assist in writing or research. However, authors must take full responsibility for all content in their paper, including any content generated by AI tools that might be construed as plagiarism or scientific misconduct. We encourage authors to explain any notable ways in which these tools were used in their research methodology. LLMs are not eligible for authorship.

Policy on Subversive/Hidden LLM Prompts

Submitting a paper with a “hidden” prompt is scientific misconduct if that prompt is intended to obtain a favorable review from an LLM. The inclusion of such a prompt is an attempt to subvert the peer-review process. Although AISTATS 2026 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion. (For an analogous example, consider that an author who tries to bribe a reviewer for a favorable review is engaging in misconduct even though the reviewer is not supposed to accept bribes.) Note that this use of hidden prompts is distinct from those intended to detect if LLMs are being used by reviewers; the latter is an acceptable use of hidden prompts.

In a recent ML conference (ICML 2025), the organizers identified a handful of submitted papers that included such prompts, including among the accepted ones, and reported their authors to the board. The AISTATS 2026 organizing committee reserves the right to both reject submitted papers that contain hidden prompts for LLMs and report their authors to the AISTATS board. The committee also reserves the right to retroactively reject papers that were initially accepted but are later found to violate this policy, before the final publication of the AISTATS proceedings.

Update September 10, 2025: added the policy statement on subversive/hidden LLM prompts.