Conference Information
COLT 2020: Annual Conference on Learning Theory
http://learningtheory.org/colt2020/
Submission Date:
2020-01-31
Notification Date:
2020-05-01
Conference Date:
2020-07-09
Location:
Graz, Austria
Years:
33
CCF: b   CORE: a*   QUALIS: a2   Viewed: 29525   Tracked: 73   Attend: 7

Conference Location
Call For Papers
The 33rd Annual Conference on Learning Theory (COLT 2020) will take place in Graz, Austria during July 9-12, 2020. 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 methods for learning, and/or online and/or stochastic optimization
    Supervised learning
    Unsupervised and semi-supervised learning
    Active and interactive learning
    Reinforcement learning
    Online learning and decision-making
    Interactions of learning theory with other mathematical fields
    Theory of artificial neural networks, including (theory of) deep learning
    High-dimensional and non-parametric statistics
    Learning with algebraic or combinatorial structure
    Theoretical analysis of probabilistic graphical models
    Bayesian methods in learning
    Game theory and learning
    Learning with system constraints (e.g., privacy, fairness, memory, communication)
    Learning from complex data (e.g., networks, time series)
    Learning in other settings (e.g., computational social science, economics)

Submissions by authors who are new to COLT are encouraged. While the primary focus of the conference is theoretical, authors may support their analysis by including relevant experimental results.

All accepted papers will be presented at the conference as both oral talks and in a poster session. At least one author of each accepted paper should be present at the conference to present the work. Accepted papers will be published electronically in the Proceedings of Machine Learning Research (PMLR). Authors of accepted papers will have the option of opting out of the proceedings in favor of a 1-page extended abstract, which will point to an open access archival version of the full paper reviewed for COLT.

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 POLICY

Conferences: In general, 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 only exception is for papers under submission to STOC 2020, as detailed below.

Dual submission with STOC 2020: The STOC 2020 notification date falls 9 days after the COLT 2020 submission deadline. In coordination with the STOC 2020 program chair, we will allow submissions that are substantially similar to papers that have been submitted to STOC 2020, provided that the authors (1) declare such dual submissions through the submission server, and (2) immediately withdraw the COLT submission if the STOC submission is accepted.

Journals: As with conferences, in general, submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to journals may not be submitted to COLT. The only exception is when the submission to COLT is a short version of a paper submitted to a journal, and not yet published. Authors must declare such dual submissions through the submission server.

FORMATTING

Submissions are limited to 12 PMLR-formatted pages, plus unlimited additional pages for references and appendices. All details, proofs and derivations required to substantiate the results must be included in the submission, possibly in the appendices. However, the contribution, novelty and significance of submissions will be judged primarily based on the main text (without appendices), and so enough details, including proof details, must be provided in the main text to convince the reviewers of the submissions' merits. Detailed formatting and submission instructions will be available on the conference website 6 weeks prior to the submission deadline: http://learningtheory.org/colt2020/.

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 an opportunity to provide a short response to the initial reviews.
Last updated by Dou Sun in 2019-11-24
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
202038812030.9%
201939311830%
20183359127.2%
20172287432.5%
20162035326.1%
20151786234.8%
20141405237.1%
20131314735.9%
20121264132.5%
20081264434.9%
2007924144.6%
20061024342.2%
20051204537.5%
20041074441.1%
2003924953.3%
2002552647.3%
20001726236%
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CCFFull NameImpact FactorPublisherISSN
Mechanism and Machine Theory3.535Elsevier0094-114X
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aIEEE Transactions on Information Theory2.728IEEE0018-9448
International Journal of Swarm Intelligence ResearchIGI Global Publishing1947-9263
bMachine Learning1.587Springer0885-6125
Measurement and Control1.229SAGE0020-2940
Law, Innovation and TechnologyTaylor & Francis1757-9961
aJournal of Machine Learning Research Microtome Publishing1532-4435
IEEE Communications Surveys & Tutorials22.97IEEE2373-745X
Full NameImpact FactorPublisher
Mechanism and Machine Theory3.535Elsevier
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ACM Transactions on Computation TheoryACM
IEEE Transactions on Information Theory2.728IEEE
International Journal of Swarm Intelligence ResearchIGI Global Publishing
Machine Learning1.587Springer
Measurement and Control1.229SAGE
Law, Innovation and TechnologyTaylor & Francis
Journal of Machine Learning Research Microtome Publishing
IEEE Communications Surveys & Tutorials22.97IEEE
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