Conference Information
COLT 2021: Annual Conference on Learning Theory
http://www.learningtheory.org/colt2021/
Submission Date:
2021-01-29
Notification Date:
2021-05-14
Conference Date:
2021-08-15
Location:
Boulder, Colorado
Years:
34
CCF: b   CORE: a*   QUALIS: a2   Viewed: 34354   Tracked: 87   Attend: 8
Conference Location
Call For Papers
The 34rd Annual Conference on Learning Theory (COLT 2021) will take place July 7-10, 2021. Assuming the circumstances allow for an in-person conference it will be held at CU Boulder, in Colorado. We invite submissions of papers addressing theoretical aspects of machine learning, broadly defined as a subject at the intersection of computer science, statistics and applied mathematics. We strongly support an inclusive view of learning theory, including fundamental theoretical aspects of learnability in various contexts, and theory that sheds light on empirical phenomena.

The topics include but are not limited to:

    Design and analysis of learning algorithms
    Statistical and computational complexity of learning
    Optimization methods for learning, including online and stochastic optimization
    Theory of artificial neural networks, including deep learning
    Theoretical explanation of empirical phenomena in learning
    Supervised learning
    Unsupervised, semi-supervised learning, domain adaptation
    Learning geometric and topological structures in data, manifold learning
    Active and interactive learning
    Reinforcement learning
    Online learning and decision-making
    Interactions of learning theory with other mathematical fields
    High-dimensional and non-parametric statistics
    Kernel methods
    Causality
    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 neuroscience, social science, economics and other subjects

Submissions by authors who are new to COLT are encouraged.

While the primary focus of the conference is theoretical, authors are welcome to support their analysis with relevant experimental results.

Accepted papers will be presented at the conference in both oral and poster sessions. At least one author of each accepted paper should present the work at the conference. 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.
Last updated by Dou Sun in 2021-04-30
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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Related Journals
CCFFull NameImpact FactorPublisherISSN
Mechanism and Machine Theory3.312Elsevier0094-114X
Language Learning & Technology2.571University of Hawaii Press1094-3501
ACM Transactions on Computation TheoryACM1942-3454
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 Theory3.312Elsevier
Language Learning & Technology2.571University of Hawaii Press
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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