AISTATS 2021 : International Conference on Artificial Intelligence and Statistics
Call For Papers
(Posting on behalf of the AISTATS 2021 organizing committee)
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. ar
The tentative dates are as follow:
Abstract submission: October 8, 2020, 08:00 AM PDT
Paper submission date: October 15, 2020, 08:00 AM PDT
Reviews released: November 23, 2020
Author rebuttals due: November 28, 2020
Final decisions: January 08, 2021
Conference dates: April 13-15, 2021
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/
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
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. 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
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
(http://arxiv.org/)) 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.
Please contact us with any questions at email@example.com.
Arindam Banerjee and Kenji Fukumizu
AISTATS 2021 Program Chairs