Monday, September 24, 2018

ALT 2019 CfP - submission deadline Sep 28

ALT 2019 CALL FOR PAPERS

The ALT 2019 conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to:

* Design and analysis of learning algorithms.
* Statistical and computational learning theory.
* Online learning algorithms and theory.
* Optimization methods for learning.
* Unsupervised, semi-supervised, online and active learning.
* Connections of learning with other mathematical fields.
* Artificial neural networks, including deep learning.
* High-dimensional and non-parametric statistics.
* Learning with algebraic or combinatorial structure.
* Bayesian methods in learning.
* Planning and control, including reinforcement learning.
* Learning with system constraints: e.g. privacy, memory or communication budget.
* Learning from complex data: e.g., networks, time series, etc.
* Interactions with statistical physics.
* Learning in other settings: e.g. social, economic, and game-theoretic.

We are also interested in papers that include viewpoints that are new to the ALT community. We welcome experimental and algorithmic papers provided they are relevant to the focus of the conference by elucidating theoretical results, or by pointing out an interesting and not well understood behavior that could stimulate theoretical analysis.
Paper submission deadline : Friday, September 28, 2018, 4:59PM EST.

Authors can submit their papers electronically via the submission page https://easychair.org/conferences/?conf=alt2019 which will be opened a few weeks before the conference submission deadline.

AWARDS
ALT 2018 will have both a best student paper award (E.M. Gold Award) and a best paper award. Authors must indicate at submission time if they wish their paper to be eligible for a student award. This does not preclude the paper to be eligible for the best paper award. The paper can be co-authored by other researchers.

TUTORIALS
We also invite proposals for a tutorial presentation. These should be dealing with a learning theory topic covered within two hours. Proposals are limited to 2 pages and should include a one page abstract as well as links to any relevant material such as existing slides or other teaching material.
Tutorials Submission Deadline : October 19, 2018.

SUBMISSION GUIDELINES
* POLICY
Each submitted paper will be reviewed by the members of the program committee and be judged on clarity, significance and originality. Joint submissions to other conferences with published proceedings are not allowed. Papers that have appeared in or are under review for other conferences are not appropriate for ALT 2019. The same policy applies to journals, unless the submission is a shorter version of a paper submitted to a journal and has not yet been published. It is, however, acceptable to submit to ALT work that has been made available as a technical report or similar, for example on https://arxiv.org/.

* FORMATTING
There is no page limit for submissions, and submissions should include all proofs and technical details necessary to understand the results. However, referees are not required to read beyond the first 12 pages when reviewing submissions. Therefore, it is recommended that the first 12 pages contain a clear presentation of the papers main contributions and at least sketches of the main arguments. All accepted papers will be published as a volume in the Proceedings of Machine Learning Research series, and will be available online during the conference. Submissions should be formatted according to the instructions on the following page: http://www.jmlr.org/format/format.html.

* REVIEW
The reviewing process is not double-blind. Authors should list their names and affiliations in their submissions.

VENUE
The conference will be held in Chicago, IL, USA from March 22-24, 2019.

CONTACT
All questions about submissions should be emailed to the PC chairs at alt2019pc@gmail.com.

CONFERENCE WEBSITE
http://alt2019.algorithmiclearningtheory.org/