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 www.aistats.org.

The Conference Program will include invited talks, contributed talks, and posters. Contributed talks and posters will be selected via a rigorous double-blind peer-review process. Accepted papers will be published in the AISTATS Conference 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
For a more detailed list of keywords, see aistats.org/keywords.php.