Connectionists: CFP: AISTATS 2017

[Aaditya Ramdas]

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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