UAI 2023

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).

The conference has been held every year since 1985. The 39th edition will be held at Carnegie Mellon University, Pittsburgh, PA, USA, on the following dates:

• Tutorials: July 31st, 2023
• Main conference: August 1st - August 3rd, 2023
• Workshops: August 4th, 2023

• 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