The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to learning and reasoning in the presence of uncertainty.

We invite papers that describe new theory, methodology and/or applications related to machine learning and statistics. We welcome submissions by authors who are new to the UAI conference, or on new and emerging topics. We also encourage submissions on applications, especially those that inspire new methodologies or novel combinations of existing methodologies, provided that some intersection with other UAI topics exists (please see subject areas below).

Submitted papers will be reviewed based on their novelty, technical quality, potential impact and clarity of writing. For papers that rely on empirical evaluations, the experimental methods and results should be clear, well executed, and reproducible. Authors are strongly encouraged to make code and data available.

Paper submission deadline February 19th, 23:59 UTC, 2021
Author response period April 14th - April 20th, 2021
Author notification May 12th, 2021

Tutorials July 26th, 2021
Main Conference July 27th - July 29th, 2021
Workshops July 30th, 2021

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