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 41st edition (UAI 2025) will be an in-person conference with virtual elements taking place in Rio de Janeiro, Brazil from July 21st to July 25th 2025.

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).

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