会议信息

COLT 2026: Annual Conference on Learning Theory

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截稿日期:
2026-02-04
通知日期:
2026-05-04
会议日期:
2026-06-29
会议地点:
San Diego, California, USA
届数:
39
CCF: b   CORE: a*   QUALIS: a2   浏览: 268635   关注: 114   参加: 14

征稿

COLT 2026 (Annual Conference on Learning Theory) is a CCF B / CORE A* / QUALIS A2 conference held in San Diego, California, USA on 2026-06-29. The paper submission deadline is 2026-02-04. Acceptance notifications are sent on 2026-05-04.

The 39th Annual Conference on Learning Theory (COLT 2026) will take place June 29-July 3, 2026 in San Diego, USA. 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 Sampling 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 Quantum learning theory 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.
最后更新 Dou Sun

录取率

Average acceptance rate: 36.7% over 17 years (2000–2020).

时间提交数录取数录取率(%)
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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ba*a2COLTAnnual Conference on Learning Theory2026-02-042026-05-042026-06-29
aa*a1ICMLInternational Conference on Machine Learning2026-01-232026-07-06
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bab1ICDTInternational Conference on Database Theory2025-09-032025-12-012026-03-24
bb1ISITInternational Symposium on Information Theory2019-01-202019-03-312019-07-07
bb2DLTInternational Conference on Developments in Language Theory2018-05-072018-06-062018-09-10
b4ICBLInternational Conference on Blended Learning2017-02-282017-03-152017-06-27
bITWInformation Theory Workshop2013-07-122013-09-09
bb1SWATScandinavian Workshop on Algorithm Theory2012-04-092012-07-04

相关期刊

CCF全称影响因子出版商ISSN
Smart Learning Environments12.1Springer2196-7091
Nano Today10.9Elsevier1748-0132
Journal of Materials Processing Technology7.5Elsevier0924-0136
Surface and Coatings Technology5.4Elsevier0257-8972
Mechanism and Machine Theory5.3Elsevier0094-114X
IEEE Transactions on Learning Technologies4.9IEEE1939-1382
Language Learning & Technology4.1University of Hawaii Press1094-3501
aIEEE Transactions on Information Theory2.9IEEE0018-9448
bMachine Learning2.9Springer0885-6125
ACM Transactions on Computation Theory0.800ACM1942-3454