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AISTATS 2023 : 26th International Conference on Artificial Intelligence and Statistics


When Mar 28, 2023 - Mar 30, 2023
Where TBD
Abstract Registration Due Oct 6, 2022
Submission Deadline Oct 13, 2022
Notification Due Jan 19, 2023
Categories    machine learning   artificial intelligence   statistics

Call For Papers

Conference dates and location TBD. Listed dates are from AISTATS 2022.

Call for Papers
We invite submissions to the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 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 submission deadline: 6 October 2022 (Anywhere on Earth)
Paper submission deadline: 13 October 2022 (Anywhere on Earth)
Supplementary materials submission deadline: 20 October 2022 (Anywhere on Earth)
Reviews released to authors: Monday, November 21, 2022
Paper decision notifications: Thursday, January 19, 2023
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, …)

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