Conference Information
COLT 2022: Annual Conference on Learning Theory
https://www.learningtheory.org/colt2022/Submission Date: |
2022-02-09 |
Notification Date: |
|
Conference Date: |
2022-07-02 |
Location: |
London, UK |
Years: |
35 |
CCF: b CORE: a* QUALIS: a2 Viewed: 48446 Tracked: 110 Attend: 12
Call For Papers
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
Last updated by Dou Sun in 2021-12-05
Acceptance Ratio
Year | Submitted | Accepted | Accepted(%) |
---|---|---|---|
2020 | 388 | 120 | 30.9% |
2019 | 393 | 118 | 30% |
2018 | 335 | 91 | 27.2% |
2017 | 228 | 74 | 32.5% |
2016 | 203 | 53 | 26.1% |
2015 | 178 | 62 | 34.8% |
2014 | 140 | 52 | 37.1% |
2013 | 131 | 47 | 35.9% |
2012 | 126 | 41 | 32.5% |
2008 | 126 | 44 | 34.9% |
2007 | 92 | 41 | 44.6% |
2006 | 102 | 43 | 42.2% |
2005 | 120 | 45 | 37.5% |
2004 | 107 | 44 | 41.1% |
2003 | 92 | 49 | 53.3% |
2002 | 55 | 26 | 47.3% |
2000 | 172 | 62 | 36% |
Best Papers
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Related Journals
CCF | Full Name | Impact Factor | Publisher | ISSN |
---|---|---|---|---|
Mechanism and Machine Theory | 3.866 | Elsevier | 0094-114X | |
Language Learning & Technology | 2.571 | University of Hawaii Press | 1094-3501 | |
a | IEEE Transactions on Information Theory | 2.728 | IEEE | 0018-9448 |
ACM Transactions on Computation Theory | ACM | 1942-3454 | ||
International Journal of Swarm Intelligence Research | IGI Global Publishing | 1947-9263 | ||
b | Machine Learning | 2.940 | Springer | 0885-6125 |
Measurement and Control | 1.229 | SAGE | 0020-2940 | |
a | Journal of Machine Learning Research | Microtome Publishing | 1532-4435 | |
Law, Innovation and Technology | Taylor & Francis | 1757-9961 | ||
IEEE Communications Surveys & Tutorials | 22.97 | IEEE | 2373-745X |
Full Name | Impact Factor | Publisher |
---|---|---|
Mechanism and Machine Theory | 3.866 | Elsevier |
Language Learning & Technology | 2.571 | University of Hawaii Press |
IEEE Transactions on Information Theory | 2.728 | IEEE |
ACM Transactions on Computation Theory | ACM | |
International Journal of Swarm Intelligence Research | IGI Global Publishing | |
Machine Learning | 2.940 | Springer |
Measurement and Control | 1.229 | SAGE |
Journal of Machine Learning Research | Microtome Publishing | |
Law, Innovation and Technology | Taylor & Francis | |
IEEE Communications Surveys & Tutorials | 22.97 | IEEE |
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