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 40th edition (UAI 2024) will be an in-person conference with virtual elements taking place in Barcelona, Spain from July 15th to July 19th 2024.
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).
Deadlines and other relevant dates can be found under important dates.
Important dates for authors:
January 8th, 2024: Paper submission starts
February 9th, 2024 (23:59 Anywhere on Earth): Paper submission deadline
April 1st-8th, 2024: Author response and discussion period
April 25th, 2024: Author notification
Papers should be submitted on OpenReview at https://openreview.net/group?id=auai.org/UAI/2024/Conference. Please see Submission Instructions for more details on how your manuscript should be formatted.
We are looking forward to building an exciting program and we aim to make the most of the advantages that a hybrid conference can create. If you have any particular positive or negative experiences that you would like to share with us, please do not hesitate to email us.
Relevant dates:
July 15th, 2024: Tutorials
July 16th-18th, 2024: Main conference
July 19th, 2024: Workshops
Below is a non-exhaustive list of relevant topics.
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
Recommender Systems
Reinforcement Learning
Relational Learning
Representation Learning
Semi-Supervised Learning
Structure Learning
Structured Prediction
Unsupervised Learning
Models
Bandits
(Dynamic) Bayesian Networks
Generative Models
Graphical Models – Directed
Graphical Models – Undirected
Graphical Models – Mixed
Markov Decision Processes
Models for Relational Data
Neural Networks
Probabilistic Circuits
Regression Models
Spatial and Spatio-Temporal Models
Temporal and Sequential Models
Topic Models and Latent Variable Models
Principles
Explainability
Causality
Computational and Statistical Trade-Offs
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