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---
layout: default
title: Call for Papers
hide: false
navigation_weight: 7
---
<h1>{{ site.conference.short_name }} {{ site.conference.year }} Call for Papers</h1>
<p>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.
</p>
<h3>Key dates:</h3>
<p>The dates are as follow:</p>
<ul>
<li><p>Abstract submission: Thursday, October 8, 2020, 08:00 AM PST </p></li>
<li><p>Submission date: Thursday, October 15, 2020, 08:00 AM PST</p></li>
<li><p>Supplementary material date: Thursday, October 22, 2020, 08:00 AM PST</p></li>
<li><p>Reviews released: Wednesday, November 25, 2020</p></li>
<li><p>Author rebuttals due: Wednesday, December 2, 2020, 20:59 PM PST</p></li>
<li><p>Final decisions: Friday, January 22, 2021</p></li>
<li><p>Camera-ready: Thursday, February 25, 2021, 16:00 PM PST</p></li>
<li><p>Conference dates: April 13-15, 2021</p></li>
</ul>
<h3>Summary</h3>
<p>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.
</p>
<p>Current website: <a href="https://www.aistats.org/aistats2021/">
https://www.aistats.org/aistats2021/</a></p>
</p>
<h1>Paper Submission:</h1>
<p><b>Proceedings track:</b> 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.</p>
<p>Solicited topics include, but are not limited to:</p>
<ul>
<li><p>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, ... </p></li>
<li><p>Classification, regression, density estimation, unsupervised and
semi-supervised learning, clustering, topic models, ... </p></li>
<li><p>Structured prediction, relational learning, logic and probability </p></li>
<li><p>Reinforcement learning, planning, control </p></li>
<li><p>Game theory, no-regret learning, multi-agent systems </p></li>
<li><p>Algorithms and architectures for high-performance computation in
AI and statistics </p></li>
<li><p>Software for and applications of AI and statistics </p></li>
<li><p>Deep learning including optimization, generalization and architectures </p></li>
<li><p>Trustworthy learning, including learning with privacy and fairness,
interpretability, and robustness </p></li>
</ul>
<h1>Formatting and Supplementary Material</h1>
<p>Submissions are limited to 8 pages <i>excluding</i> references using the LaTeX
style file we provide below (9 pages for camera-ready submissions).
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.</p>
<p>Submissions will be through CMT (<a href="https://cmt3.research.microsoft.com/AISTATS2021/">
https://cmt3.research.microsoft.com/AISTATS2021/</a>) and will be open a
month before the abstract submission deadline.</p>
</p>
<!-- <p>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.</p>
-->
<p>Formatting information (including LaTeX style files) is <a
href="AISTATS2021PaperPack.zip">here</a>. 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.</p>
<h1>Reviewer Nomination</h1>
<p>For each submission, the authors will be requested to nominate at
least one of the authors as a reviewer for AISTATS 2021. Nominated
reviewers are expected to have sufficient expertise in the relevant field.
Kindly understand that by a recent increase of submissions, we need more
reviewers than previous years.</p>
<h1>Anonymization Requirements</h1>
<p>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.</p>
<p>Previous tech-report or workshop versions of a paper can similarly present a
problem for anonymization. We suggest <i>leaving out</i> 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 <a href="http://arxiv.org/">arXiv</a>) and papers
in workshops without archival proceedings do not count as prior publication.</p>
<h1>Previous or Concurrent Submissions</h1>
<p>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. Submissions as extended abstracts (4 pages
or less), to workshops or non-archival venues (without a proceedings), or
to arXiv, will not be considered a concurrent submission. 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). </p>
<p>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.</p>
<p>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.</p>
<h1>Confidentiality</h1>
<p>The reviewers and area-chairs of your paper will have access to your paper and supplementary material. In addition, the program chairs and workflow chairs will have access to all the papers. Everyone having access to papers and supplementary materials will be instructed to keep them confidential during the review process and delete them after the final decisions.</p>
<p>Reviews will be visible to area chairs, program chairs, and workflow chairs throughout the process. Reviewers will get access to other reviews for a paper after they have submitted their own review.</p>
<p>Author names will be visible to area chairs and program chairs. Reviewers will not know the author names at any stage of the process. Reviewer names are visible to the area chair (and program chairs), but the reviewers will not know names of other reviewers.</p>
<br>
<p>Arindam Banerjee and Kenji Fukumizu<br>AISTATS 2021 Program Chairs</p>
<p> </p>
<br>
<br>