Connectionists: AISTATS 2017: Accepted Papers and Early Registration

[Aaditya Ramdas]

The final list of accepted papers can now be accessed online
<http://www.aistats.org/accepted.html>, with camera-ready versions of
papers to be uploaded to the website next month.


Please register early for cheaper rates (before Mar 7).


For more information regarding AISTATS 2017, please see the email below
(call for papers), or visit the AISTATS website.





On Sun, Aug 21, 2016 at 3:12 PM, Aaditya Ramdas <aramdas at berkeley.edu>
wrote:

>
> AISTATS <http://aistats.org/> is an interdisciplinary gathering of
> researchers at the intersection of artificial intelligence, machine
> learning, statistics, and related areas. The 20th International Conference
> on Artificial Intelligence and Statistics (AISTATS
> <http://www.aistats.org/>) will take place in Fort Lauderdale, Florida,
> USA from *April 20-22, 2017*.
>
> The deadline for paper submission is *Oct 13, 2016*, with final decisions
> made on Jan 24, 2017.
>
> New this year:
>
>    1. *Fast-track for Electronic Journal of Statistics*: Authors of a
>    small number of accepted papers will be invited to submit an extended
>    version for fast-track publication in a special issue of the Electronic
>    Journal of Statistics (EJS) after the AISTATS decisions are out. Details on
>    how to prepare such extended journal paper submission will be announced
>    after the AISTATS decisions.
>
>    2. *Review-sharing with NIPS*: Papers previously submitted to NIPS
>    2016 are required to declare their previous NIPS paper ID, and optionally
>    supply a one-page letter of revision (similar to a revision letter to
>    journal editors; anonymized) in supplemental materials. AISTATS reviewers
>    will have access to the previous anonymous NIPS reviews. Other than this,
>    all submissions will be treated equally.
>
>
> *Paper Submission:* Electronic submission of PDF papers is required. The
> main part of the paper (single PDF up to 5Mb) may be up to 8 double-column
> pages in length including tables/figures. References only can exceed the 8
> page limit. The main part should have enough information so that reviewers
> are able to judge the correctness and merit of the paper. Authors may
> optionally submit supplementary material (up to 10Mb) as a single zip file,
> containing additional proofs, audio, images, video, data or source code.
> Reviewing any supplementary material is up to the discretion of the
> reviewers.
>
> *Dual Submissions Policy:* Submissions that are identical (or
> substantially similar) to versions that have been previously published, or
> accepted for publication, or that have been submitted in parallel to other
> conferences or journals are not appropriate for AISTATS and violate our
> dual submission policy. Exceptions to this rule are the following: (a) it
> is acceptable to submit work that has been made available as a technical
> report or similar, e.g., on arXiv, without citing it (to preserve
> anonymity). (b) Submission is permitted for papers presented or to be
> presented at conferences or workshops without proceedings (e.g., ICML or
> NIPS workshops), or with only abstracts published. The dual-submission
> rules apply during the whole AISTATS review period until the authors have
> been notified about the decision on their paper.
>
> *Double-blind review:* Papers will be selected via a rigorous
> double-blind peer-review process (the reviewers will not know the
> identities of the authors, and vice versa). It will be up to the authors to
> ensure the proper anonymization of their paper and supplemental materials.
> Violation of the above rules may lead to rejection without review. One
> round of author rebuttal will occur with the initial reviews available to
> the authors.
>
> *Evaluation Criteria:* Submissions will be judged on the basis of
> technical quality, novelty, potential impact, and clarity. Typical papers
> often (but not always) consist of a mix of algorithmic, theoretical and
> experimental results, in varying proportions. Results will be judged on the
> degree to which they have been objectively established and/or their
> potential for scientific and technological impact.
>
> *Publication and presentation:* All accepted papers will be presented at
> the conference as posters, with a few selected for additional oral
> presentation. All accepted papers will be treated equally when published in
> the AISTATS Conference Proceedings (Journal of Machine Learning Research
> Workshop and Conference Proceedings series). At least one author of each
> accepted paper must register and attend AISTATS. A small number of accepted
> papers will be invited to submit an extended version for fast-track
> publication in a special issue of the Electronic Journal of Statistics
> (EJS) journal after the AISTATS decisions are out.
>
> *Topics:* Since its inception in 1985, the primary goal of AISTATS has
> been to promote the exchange of ideas from artificial intelligence, machine
> learning, and statistics. We encourage the submission of all papers in
> keeping of this objective. Solicited topics include, but are not limited to:
>
>    - Supervised, unsupervised and semi-supervised learning, kernel and
>    Bayesian methods
>    - Stochastic processes, hypothesis testing, causality, time-series,
>    nonparametrics, asymptotic theory
>    - Graphical models and inference, manifold learning and embedding,
>    network analysis, statistical analysis of deep learning
>    - Sparse models and compressed sensing, information theory
>    - Reinforcement learning, planning, control, multi-agent systems,
>    logic and probability, relational learning
>    - Learning theory, game theoretic learning, online learning, bandits,
>    learning for mechanism design
>    - Convex and non-convex optimization, discrete optimization, Bayesian
>    optimization
>    - Algorithms and architectures for high-performance computing
>    - Applications in biology, cognition, computer vision, natural
>    language, neuroscience, robotics, etc.
>    - Topological data analysis, selective inference, experimental design,
>    interactive learning, optimal teaching, and other emerging topics
>
>
> ---
> Aaditya Ramdas
> www.cs.berkeley.edu/~aramdas
>
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