Conference Information
UAI 2020: Conference on Uncertainty in Artificial Intelligence
http://auai.org/uai2020/
Submission Date:
2020-02-20
Notification Date:
2020-05-14
Conference Date:
2020-08-03
Location:
Toronto, Ontario, Canada
Years:
36
CCF: b   CORE: a*   QUALIS: a1   Viewed: 20210   Tracked: 77   Attend: 4

Conference Location
Call For Papers
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

Application

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

Learning

    Active Learning
    Classification
    Clustering
    Deep Learning
    Nonparametric Bayes
    Online and Anytime Learning
    Parameter Estimation
    Probabilistic Generative Models
    Ranking
    Recommender Systems
    Regression
    Reinforcement Learning
    Relational Learning
    Semi-Supervised Learning
    Structure Learning
    Structured Prediction
    Theory
    Unsupervised

Methodology

    Bayesian Methods
    Calibration
    Elicitation
    Evaluation
    Human Expertise and Judgement
    Probabilistic Programming
    Relational
    Spatial
    Temporal or Sequential

Models

    Bayesian Networks
    Directed Graphical Models
    Dynamic Bayesian Networks
    Markov Decision Processes
    Mixed Graphical Models
    Topic Models
    Undirected Graphical Models

Principles

    Causality
    Cognitive Models
    Decision Theory
    Game Theory
    Information Theory
    Probability Theory
    Statistical Theory

Representation

    Constraints
    Dempster-Shafer
    Influence Diagrams
Last updated by Dou Sun in 2019-12-26
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
20112859633.7%
20102608833.8%
20092437631.3%
20082567228.1%
20062136831.9%
20052438635.4%
20042532610.3%
20032292510.9%
20021926634.4%
2000843035.7%
19991507751.3%
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