UAI 2021 - Subject Areas


When submitting a paper, you will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. The terms have been grouped to provide a somewhat systematic overview of topics relevant to the UAI conference. For example, a paper about a new approximate inference algorithm for dynamic Bayesian network with applications to a problem in biology could select the combination primary = Models: (Dynamic) Bayesian networks, secondary = [Application: Computational Biology, Algorithms: Approximate Inference] and so on.

The list of subject areas appears to authors and reviewers in the CMT conference management system. Below you find a list 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
  • 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