Conference Information
AISTATS 2021: International Conference on Artificial Intelligence and Statistics
http://aistats.org/aistats2021/
Submission Date:
2020-10-08
Notification Date:
2021-01-08
Conference Date:
2021-04-13
Location:
San Diego, California, USA
Years:
24
CCF: c   CORE: a   Viewed: 23702   Tracked: 100   Attend: 15

Conference Location
Call For Papers
We invite submissions to the 2021 International Conference on Artificial Intelligence and Statistics (AISTATS), and welcome paper submissions on artificial intelligence, machine learning, statistics, and related areas. 

Summary

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.

Current website: https://www.aistats.org/aistats2021/

Paper Submission:

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

Formatting and Supplementary Material

Submissions are limited to 8 pages excluding references using the LaTeX style file we provide below. The number of pages containing citations alone is not limited. You can also submit a single file of additional supplementary material which may be either a pdf file (such as proof details) or a zip file for other formats/more files (such as code or videos). Note that reviewers are under no obligation to examine your supplementary material. If you have only one supplementary pdf file, please upload it as is; otherwise gather everything to the single zip file.

Submissions will be through CMT ( https://cmt3.research.microsoft.com/AISTATS2021/) and will be open a month before the abstract submission deadline.

Formatting information (including LaTeX style files) will be made available. We do not support submission in preparation systems other than LaTeX. Please do not modify the layout given by the style file. If you have questions about the style file or its usage, please contact the publications chair.

Anonymization Requirements

The AISTATS review process is double-blind. Please remove all identifying information from your submission, including author names, affiliations, and any acknowledgments. Self-citations can present a special problem: we recommend leaving in a moderate number of self-citations for published or otherwise well-known work. For unpublished or less-well-known work, or for large numbers of self-citations, it is up to the author's discretion how best to preserve anonymity. Possibilities include leaving out a citation altogether, including it but replacing the citation text with "removed for anonymous submission," or leaving the citation as-is; authors should choose for each citation the treatment which is least likely to reveal authorship.

Previous tech-report or workshop versions of a paper can similarly present a problem for anonymization. We suggest leaving out any identifying information for such versions, but bringing them to the attention of the program committee via the submission page. Reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.

Previous or Concurrent Submissions

Submitted manuscripts should not have been previously published in a journal or in the proceedings of a conference, and should not be under consideration for publication at another conference at any point during the AISTATS review process. It is acceptable to have a substantially extended version of the submitted paper under consideration simultaneously for journal publication, so long as the journal version's planned publication date is in May 2021 or later, the journal submission does not interfere with AISTATS's right to publish the paper, and the situation is clearly described at the time of AISTATS submission. Please describe the situation in the appropriate box on the submission page (and do not include author information in the submission itself, to avoid accidental unblinding).

As mentioned above, reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.

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.
Last updated by Dou Sun in 2020-09-04
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