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
Sponsors
