Call for Papers

They 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 2023
• Author rebuttals due: 5 December 2023 (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, online leaning 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, …)