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: 287028   Tracked: 159   Attend: 17

Conference Partner Index (CP-I)

89.8 / 100
Ranked #62 of 5,647 conferences · Top 2%

#7 of 734 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
100
Submission selectivity (20%)
79
Editions held (20%)
100
Community attention (10%)
71
Public record completeness (15%)
80

Inputs used: Listed as CCF B, ICORE A, QUALIS A1 · Acceptance rate: 29.5% (mean of 5 editions on file) · Editions on record: 42 · Researchers following it here: 159 · Researchers who opened this page in the past 24 months: 17

Missing from the public record: Past editions (+3.0)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-03

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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