AISTATS 2021 : International Conference on Artificial Intelligence and Statistics

in Conferences   Posted on November 2, 2020 

Conference Information

Submission Deadline Thursday 08 Oct 2020 Proceedings indexed by :
Conference Dates Apr 13, 2021 - Apr 15, 2021
Conference Address Virtual, United States
Conference & Submission Link https://aistats.org/aistats2021/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 9.9
#Contributing Top Scientists 116
#Papers published by Top Scientists 177
Google Scholar H5-index 57
Number of Editions: 24 (Since 1991)
Guide2Research Overall Ranking: 25
Category Rankings
Computational Theory and Mathematics 2
Databases & Information Systems 3
Machine Learning & Artificial Intelligence 14
Proceedings http://proceedings.mlr.press/
DBLP Page https://dblp.uni-trier.de/db/conf/aistats

Conference Call for Papers

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

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