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. We encourage the submission of all papers which are in keeping with this objective at AISTATS.
Proceedings track: This 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:
Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, …
Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, …
Structured prediction, relational learning, logic and probability
Reinforcement learning, planning, control
Game theory, no-regret learning, multi-agent systems
Algorithms and architectures for high-performance computation in AI and statistics
Software for and applications of AI and statistics
Deep learning including optimization, generalization and architectures
Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness
For a more detailed list of keywords, please see here.
Submission Requirements for Proceedings Track:
Electronic submission of papers is required. Papers may be up to 8 double-column pages in length, excluding references. Authors may optionally submit also supplementary material. Formatting and submission information is available at here.
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.