Conference Information

UAI 2026: Conference on Uncertainty in Artificial Intelligence

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Submission Date:
2026-02-25
Notification Date:
2026-06-01
Conference Date:
2026-08-17
Location:
Amsterdam, the Netherlands
Years:
42
CCF: B   ICORE: A   QUALIS: A1   Viewed: 251880   Tracked: 159   Attend: 17

Call For Papers

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
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 30.3% over 23 years (1999–2025).

YearSubmittedAcceptedAccepted(%)
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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