Conference Information
COLT 2024: Annual Conference on Learning Theory
https://www.learningtheory.org/colt2024/Submission Date: |
2024-02-09 |
Notification Date: |
2024-05-10 |
Conference Date: |
2024-06-30 |
Location: |
Edmonton, Canada |
Years: |
37 |
CCF: b CORE: a* QUALIS: a2 Viewed: 56696 Tracked: 113 Attend: 13
Call For Papers
The 37th Annual Conference on Learning Theory (COLT 2024) will take place June 30th-July 3rd, 2024 in Edmonton, Canada. 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. 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 2023-12-24
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 | 4.500 | Elsevier | 0094-114X | |
Language Learning & Technology | 2.571 | University of Hawaii Press | 1094-3501 | |
ACM Transactions on Computation Theory | ACM | 1942-3454 | ||
a | IEEE Transactions on Information Theory | 2.728 | IEEE | 0018-9448 |
International Journal of Swarm Intelligence Research | IGI Global Publishing | 1947-9263 | ||
b | Machine Learning | 4.300 | Springer | 0885-6125 |
Measurement and Control | 1.229 | SAGE | 0020-2940 | |
Law, Innovation and Technology | Taylor & Francis | 1757-9961 | ||
a | Journal of Machine Learning Research | Microtome Publishing | 1532-4435 | |
IEEE Communications Surveys & Tutorials | 22.97 | IEEE | 2373-745X |
Full Name | Impact Factor | Publisher |
---|---|---|
Mechanism and Machine Theory | 4.500 | Elsevier |
Language Learning & Technology | 2.571 | University of Hawaii Press |
ACM Transactions on Computation Theory | ACM | |
IEEE Transactions on Information Theory | 2.728 | IEEE |
International Journal of Swarm Intelligence Research | IGI Global Publishing | |
Machine Learning | 4.300 | Springer |
Measurement and Control | 1.229 | SAGE |
Law, Innovation and Technology | Taylor & Francis | |
Journal of Machine Learning Research | Microtome Publishing | |
IEEE Communications Surveys & Tutorials | 22.97 | IEEE |
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