For Participants

For Authors and Reviewers



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

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