UAI 2021

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 37th edition (UAI 2021) will take place online from 27 to 30 July 2021.

We invite papers that describe novel theory, methodology and applications related to artificial intelligence, machine learning and statistics. Please see a list of possible topics here. Papers will be assessed in a rigorous double-blind peer-review process by our program committee and senior program committee, based on their novelty, technical quality, potential impact and clarity of writing. Authors are strongly encouraged to make code and data available. Please see the submission instructions for detailed information about the submission process.

All accepted papers will be presented in poster sessions and spotlight presentations in a single plenary track to ensure good visibility. Selected papers will have longer presentations and an assigned discussant to foster debate. All accepted papers will be published in a volume of Proceedings of Machine Learning Research (PMLR).

• 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