The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to learning and reasoning in the presence of uncertainty. The conference has been held every year since 1985. The upcoming 42nd edition (UAI 2026) will be an in-person conference taking place in Amsterdam, The Netherlands, on these dates:

Tutorials: Monday, August, 17th, 2026
Main conference: Tuesday, August 18th to Thursday, August 20th, 2026
Workshops: Friday, August, 21st, 2026

We invite papers that describe novel theory, methodology and applications related to artificial intelligence, machine learning and statistics. Papers will be assessed in a rigorous double-blind peer-review process, based on the criteria of technical correctness, novelty, whether claims are backed up convincingly, and clarity of writing. Authors are strongly encouraged to make code and data available.

All accepted papers will be presented in poster sessions and spotlight presentations (physically or remotely). Selected papers will have longer presentations. All accepted papers will be published in a volume of Proceedings of Machine Learning Research (PMLR).

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