Información de la conferencia

COLT 2026: Annual Conference on Learning Theory

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Día de Entrega:
2026-02-04
Fecha de Notificación:
2026-05-04
Fecha de conferencia:
2026-06-29
Ubicación:
San Diego, California, USA
Ediciones:
39
CCF: B   ICORE: A*   QUALIS: A2   Vistas: 367002   Seguidores: 114   Asistentes: 14

Índice Conference Partner (CP-I)

89,2 / 100
Puesto n.º 83 de 5.682 congresos · 2% superior

N.º 9 de 739 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
100
Selectividad en la revisión (20%)
79
Ediciones celebradas (20%)
99
Atención de la comunidad (10%)
65
Integridad del registro público (15%)
80

Datos utilizados: Categorías: CCF B, QUALIS A2 · Tasa de aceptación: 29.3% (media de 5 ediciones registradas) · Ediciones documentadas: 39 · Investigadores que lo siguen aquí: 114 · Investigadores que abrieron esta página en los últimos 24 meses: 12

Falta en el registro público: Ediciones anteriores (+3,0)
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Solicitud de Artículos

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.
Última actualización por Dou Sun el

Tasa de aceptación

Tasa de aceptación media: 36.7% en 17 años (2000–2020).

AñoEnviadosAceptadosAceptados(%)
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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