Información de la conferencia

UAI 2026: Conference on Uncertainty in Artificial Intelligence

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UAI
Día de Entrega:
2026-02-25
Fecha de Notificación:
2026-06-01
Fecha de conferencia:
2026-08-17
Ubicación:
Amsterdam, the Netherlands
Ediciones:
42
CCF: B   ICORE: A   QUALIS: A1   Vistas: 265506   Seguidores: 159   Asistentes: 17

Solicitud de Artículos

UAI 2026 (Conference on Uncertainty in Artificial Intelligence) is a CCF B / ICORE A / QUALIS A1 conference held in Amsterdam, the Netherlands on 2026-08-17. The paper submission deadline is 2026-02-25. Acceptance notifications are sent on 2026-06-01.

Subject Areas Below you find a non-exhaustive list of relevant topics for your reference. Algorithms Approximate Inference Bayesian Methods Belief Propagation Exact Inference Kernel Methods Missing Data Handling Monte Carlo Methods Optimization - Combinatorial Optimization - Convex Optimization - Discrete Optimization - Non-Convex Probabilistic Programming Randomized Algorithms Spectral Methods Variational Methods Applications Cognitive Science Computational Biology Computer Vision Crowdsourcing Earth System Science Education Forensic Science Healthcare Natural Language Processing Neuroscience Planning and Control Privacy and Security Robotics Social Good Sustainability and Climate Science Text and Web Data Learning Active Learning Adversarial Learning Causal Learning Classification Clustering Compressed Sensing and Dictionary Learning Deep Learning Density Estimation Dimensionality Reduction Ensemble Learning Feature Selection Hashing and Encoding Multitask and Transfer Learning Online and Anytime Learning Policy Optimization and Policy Learning Ranking Reinforcement Learning and Bandits Relational Learning Representation Learning Semi-Supervised Learning Structure Learning Structured Prediction Unsupervised Learning Models Foundation Models Generative Models Graphical Models Models for Relational Data Neural Networks Probabilistic Circuits Regression Models Spatial, Temporal and Spatio-Temporal Models Topic Models and Latent Variable Models Principles Causality Computational and Statistical Trade-Offs Explainability Fairness Privacy Reliability Robustness (Structured) Sparsity Representation Constraints Dempster-Shafer (Description) Logics Imprecise Probabilities Influence Diagrams Knowledge Representation Languages Theory Computational Complexity Control Theory Decision Theory Game Theory Information Theory Learning Theory Probability Theory Statistical Theory
Última actualización por Dou Sun el

Tasa de aceptación

Tasa de aceptación media: 30.3% en 23 años (1999–2025).

AñoEnviadosAceptadosAceptados(%)
202575023030.7%
202474420026.9%
202377824331.2%
202271223032.3%
202177720526.4%
202051514227.6%
201945011826.2%
201833710430.9%
20172828730.9%
20162758530.9%
20152919934%
20142929432.2%
20112859633.7%
20102608833.8%
20092437631.3%
20082567228.1%
20062136831.9%
20052438635.4%
20042532610.3%
20032292510.9%
20021926634.4%
2000843035.7%
19991507751.3%

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Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
ACM Computing Surveys28.0ACM0360-0300
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
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
BComputers & Security5.4Elsevier0167-4048
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050

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