Conference Information
COLT 2025: Annual Conference on Learning Theory
https://learningtheory.org/colt2025/index.html
Submission Date:
2025-02-06
Notification Date:
2025-05-02
Conference Date:
2025-06-30
Location:
Lyon, France
Years:
38
CCF: b   CORE: a*   QUALIS: a2   Viewed: 63732   Tracked: 112   Attend: 13

Call For Papers
The 38th Annual Conference on Learning Theory (COLT 2025) will take place June 30th-July 4th, 2025 in Lyon, France. 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 2024-12-18
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%
Related Conferences
CCFCOREQUALISShortFull NameSubmissionNotificationConference
baa2ECAIEuropean Conference on Artificial Intelligence2024-04-192024-07-042024-10-19
BMSBInternational Symposium on Broadband Multimedia Systems and Broadcasting2024-12-062025-01-242025-06-11
cSPICESoftware Process Improvement and Capability Determination2017-06-122017-07-072017-10-04
NordiCHINordic forum for Human-Computer Interaction2020-04-222020-06-232020-10-25
PEPSCPower Electronics and Power System Conference2024-07-102024-08-102024-11-07
ICST'International Conference on Sensing Technology2018-07-212018-08-302018-12-03
SEASInternational Conference on Software Engineering and Applications2023-09-092023-09-192023-09-23
CCWCIEEE Annual Computing and Communication Workshop and Conference2024-11-152024-11-272025-01-06
PARCInternational Conference on Power Electronics & IoT Applications in Renewable Energy and Its Control2020-01-152020-01-252020-02-28
WebMediaBrazilian Symposium on Multimedia and the Web2020-08-012020-10-102020-11-30
Related Journals
CCFFull NameImpact FactorPublisherISSN
Combinatorial Chemistry & High Throughput ScreeningBentham1386-2073
Law, Innovation and TechnologyTaylor & Francis1757-9961
bParallel Computing2.000Elsevier0167-8191
Computing3.300Springer0010-485X
International Journal of Adaptive Control and Signal Processing3.900Wiley-Blackwell0890-6327
International Journal of Fuzzy Logic and Intelligent SystemsKorean Institute of Intelligent Systems1598-2645
International Journal of Computer Science and engineering Survey AIRCC0976-3252
Optimization Methods and Software1.400Taylor & Francis1055-6788
Complexity1.700Hindawi1076-2787
Optics & Laser Technology4.600Elsevier0030-3992
Recommendation