UAI 2013 - 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 |
| Algorithms: Belief Propagation |
| Algorithms: Distributed and Parallel |
| Algorithms: Exact Inference |
| Algorithms: Graph Theory |
| Algorithms: Heuristics |
| Algorithms: MCMC methods |
| Algorithms: Optimization |
| Algorithms: Other |
| Algorithms: Software and Tools |
| Applications: Biology |
| Applications: Databases |
| Applications: Decision Support |
| Applications: Diagnosis and Reliability |
| Applications: Economics |
| Applications: General |
| Applications: Medicine |
| Applications: Planning and Control |
| Applications: Privacy and Security |
| Applications: Robotics |
| Applications: Sensor Data |
| Applications: Social Network Analysis |
| Applications: Speech |
| Applications: Sustainability and Climate |
| Applications: Text and Web Data |
| Applications: User Models |
| Applications: Vision |
| Data: Multivariate |
| Data: Other |
| Data: Relational |
| Data: Spatial |
| Data: Temporal or Sequential |
| Learning: Active Learning |
| Learning: Classification |
| Learning: Clustering |
| Learning: Deep Learning |
| Learning: General |
| Learning: Nonparametric Bayes |
| Learning: Online and Anytime Learning |
| Learning: Other |
| Learning: Parameter Estimation |
| Learning: Probabilistic Generative Models |
| Learning: Ranking |
| Learning: Recommender Systems |
| Learning: Regression |
| Learning: Reinforcement Learning |
| Learning: Relational Learning |
| Learning: Scalability |
| Learning: Semi-Supervised Learning |
| Learning: Structure Learning |
| Learning: Structured Prediction |
| Learning: Theory |
| Learning: Unsupervised |
| Methodology: Bayesian Methods |
| Methodology: Calibration |
| Methodology: Elicitation |
| Methodology: Evaluation |
| Methodology: Human Expertise and Judgement |
| Methodology: Other |
| Methodology: Probabilistic Programming |
| Models: Bayesian Networks |
| Models: Directed Graphical Models |
| Models: Dynamic Bayesian Networks |
| Models: Markov Decision Processes |
| Models: Mixed Graphical Models |
| Models: Other |
| Models: Undirected Graphical Models |
| Principles: Causality |
| Principles: Cognitive Models |
| Principles: Decision Theory |
| Principles: Game Theory |
| Principles: Information Theory |
| Principles: Other |
| Principles: Probability Theory |
| Principles: Statistical Theory |
| Representation: Constraints |
| Representation: Dempster-Shafer |
| Representation: Fuzzy Logic |
| Representation: Influence Diagrams |
| Representation: Non-Probabilistic Frameworks |
| Representation: Probabilistic |
