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COLT 2026: Annual Conference on Learning Theory

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COLT
投稿締切日:
2026-02-04
通知日:
2026-05-04
開催日:
2026-06-29
開催地:
San Diego, California, USA
開催回数:
39
CCF: B   ICORE: A*   QUALIS: A2   閲覧: 340088   フォロー: 114   参加: 14

論文募集

COLT 2026 (Annual Conference on Learning Theory) is a CCF B / ICORE 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.
最終更新：Dou Sun（）

採択率

平均採択率: 36.7% 17 年間 (2000–2020).

年投稿数採択数採択率(%)
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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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
AIEEE Transactions on Pattern Analysis and Machine Intelligence20.8IEEE0162-8828
AArtificial Intelligence4.6Elsevier0004-3702
AACM Transactions on Database Systems1.7ACM0362-5915
AIEEE Transactions on Software Engineering5.6IEEE0098-5589
AACM Transactions on Software Engineering and Methodology6.2ACM1049-331x
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.6Elsevier1389-1286

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