The 30th Annual Conference on Learning Theory (COLT 2017) will take place in Amsterdam, the Netherlands, on July 7-10, 2017 (with a welcome reception on the 6th)

We invite submissions of papers addressing theoretical aspects of machine learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:

Design and analysis of learning algorithms
Statistical and computational complexity of learning
Optimization models and algorithms for learning
Unsupervised, semi-supervised, and active learning
Online learning
Artificial neural networks, including deep learning
Learning with large-scale datasets
Decision making under uncertainty
Bayesian methods in learning
High dimensional and non-parametric statistical inference
Planning and control, including reinforcement learning
Learning with additional constraints: e.g. privacy, memory or communication budget
Learning in other settings: e.g. social, economic, and game-theoretic
Analysis and applications of learning theory in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision,
information retrieval
Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, the authors may support their analysis by including relevant experimental results.

All accepted papers will be presented in a single track at the conference. At least one of each paper’s authors should be present at the conference to present the work. Accepted papers will be published electronically in the JMLR Workshop and Conference Proceedings series. The authors of accepted papers will have the option of opting-out of the proceedings in favor of a 1-page extended abstract. The full paper reviewed for COLT will then be placed on the arXiv repository.

Paper Awards: COLT will award both best paper and best student paper awards. To be eligible for the best student paper award, the primary contributor(s) must be full-time students at the time of submission. For eligible papers, authors must indicate at submission time if they wish their paper to be considered for a student paper award. The program committee may decline to make these awards, or may split them among several papers.

Dual Submissions: Submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings may not be submitted to COLT. The same policy applies to journals, unless the submission is a short version of a paper submitted to a journal, and not yet published. Authors must declare such dual submissions either through the Easychair submission form, or via email to the program chairs.

Page limit: There is no formal page limit for the submission, and it should contain all details, proofs and derivations required to substantiate the results. However, the reviewers are not required to read beyond the first 12 pages when evaluating the submission. Therefore, the first 12 (or fewer) pages should contain a concise and clear presentation of the paper’s contributions, as well as sufficient detail to convince the reviewers of the paper’s merits. Additional pages should be used for references, technical details and derivations, and not for presenting key new ideas or contributions. Further formatting and submission instructions will be posted on the conference’s website.

Rebuttal Phase: As in previous years, there will be a rebuttal phase during the review process. Initial reviews will be sent to authors before final decisions have been made. Authors will have the opportunity to provide a short response on the PC’s initial evaluation.

Open Problems Session: We also invite submission of open problems. A separate call for open problems will be made available on the conference website.

Important Dates:

Paper submission deadline: February 17, 2017, 11:00 PM EST
Author feedback: April 7-12, 2017
Author notification: May 5, 2017
Conference: July 7-10, 2017 (welcome reception on the 6th)