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.

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, …)