UAI 2020 - Subject Areas

When an author submits a paper, they 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 = dynamic Bayesian network, secondary = [application/biology, algorithms/approximate inference] and so on.

For reference, below is the list of subject areas that will appear to authors and reviewers in the CMT conference management system:

Algorithms
  • Approximate Inference
  • Belief Propagation
  • Exact Inference
  • MCMC methods
  • Optimization
Application
  • Biology
  • Education
  • Health
  • Planning and Control
  • Privacy and Security
  • Fairness
  • Robotics
  • Natural Language Processing
  • Sustainability and Climate
  • Text and Web Data
  • User Models
  • Vision
Learning
  • Active Learning
  • Classification
  • Clustering
  • Deep Learning
  • Nonparametric Bayes
  • Online and Anytime Learning
  • Parameter Estimation
  • Probabilistic Generative Models
  • Ranking
  • Recommender Systems
  • Regression
  • Reinforcement Learning
  • Relational Learning
  • Semi-Supervised Learning
  • Structure Learning
  • Structured Prediction
  • Theory
  • Unsupervised
Methodology
  • Bayesian Methods
  • Calibration
  • Elicitation
  • Evaluation
  • Human Expertise and Judgement
  • Probabilistic Programming
  • Relational
  • Spatial
  • Temporal or Sequential
Models
  • Bayesian Networks
  • Directed Graphical Models
  • Dynamic Bayesian Networks
  • Markov Decision Processes
  • Mixed Graphical Models
  • Topic Models
  • Undirected Graphical Models
Principles
  • Causality
  • Cognitive Models
  • Decision Theory
  • Game Theory
  • Information Theory
  • Probability Theory
  • Statistical Theory
Representation
  • Constraints
  • Dempster-Shafer
  • Influence Diagrams