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
Probabilistic Programming

Application

Biology
Computer Vision
Earth System Science
Education
Fairness
Health
Natural Language Processing
Planning and Control
Privacy and Security
Robotics
Sustainability and Climate
Text and Web Data
User Models

Learning

Active Learning
Bayesian Methods
Classification
Clustering
Deep Learning
Kernel Methods
Learning Theory
Method-of-Moments or Spectral Methods
Missing Data
Nonparametric Bayes
Online and Anytime Learning
Probabilistic Generative Models
Ranking
Recommender Systems
Regression
Reinforcement Learning
Relational Learning
Semi-Supervised Learning
Structure Learning
Structured Prediction
Unsupervised Learning

Models

Bayesian Networks
Dynamic Bayesian Networks
Markov Decision Processes
Mixed Graphical Models
Relational data
Spatial or spatio-temporal data
Temporal or sequential data
Topic Models or other Latent Variable Models
Undirected Graphical Models

Principles

Causality
Cognitive Models
Decision Theory
Distributional Robustness
Game Theory
Human Expertise and Judgement
Information Theory
Invariance
Model Evaluation
Model Calibration
Probability Theory
Statistical Theory

Representation

Constraints
Dempster-Shafer
Influence Diagrams

Sponsors